Copyright is owned by the Author of the thesis.  Permission is given for 
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An Empirical Examination of Industry Returns 
for Evidence of Cyclical Performance  
 
A thesis presented in partial fulfillment of the requirements for the degree 
 
 
 
Doctor of Philosophy 
at 
School of Economics and Finance 
Massey University, Albany 
 
 
Jeffrey Scott Stangl 
2012
 i 
Abstract 
This dissertation provides three empirical studies of industry performance related to 
different financial market cycles. Popular belief holds that industries provide systematic 
cyclical performance. Such systematic performance would present a challenge to basic 
assumptions of market efficiency. The three industry cycles investigated are sentiment 
cycles, political cycles, and business cycles. 
The first study investigates the interaction between three popular investor-sentiment 
measures and industry performance. Investor sentiment has a widespread and systematic 
effect on industry performance. Similar to prior market studies, investor sentiment 
predicts short-term industry mispricing. Predictable long-term industry reversals are 
weaker. Moreover, the effect of investor sentiment is widespread, with limited evidence 
of cross-sectional industry differences. Unlike prior market studies, there is no evidence 
of a relationship between investor sentiment and industry characteristics that serve as a 
proxy for valuation uncertainty. Lastly, an industry rotation strategy based on investor 
sentiment generates marginal outperformance, which turnover and transaction costs 
would consume. Results generally show that investor sentiment has a market-wide 
effect, questioning its usefulness in timing industry investments. 
The second study examines industry returns for presidential election cycles. Risk-
adjusted industry returns provide no evidence of political cycles previously documented 
in the U.S. stock market. In spite of the existence of market-wide effects, realized 
industry returns exhibit neither systematic nor persistent outperformance related to a 
president’s political affiliation or the year of a president’s term. Expected industry 
performance is equally unaffected by political cycles, exhibiting no systematic response 
to presidential elections and indicating that the market does not systematically price a 
president’s political affiliation in industry returns. The study’s results question the 
popular belief that certain industries systematically perform better under Democrats or 
Republicans and provide evidence that political cycles are solely a market-wide 
phenomenon best explained at a macroeconomic level. 
The third study investigates industry returns for systematic business-cycle 
performance. Popular guidance holds that sectors/industries provide systematic 
 ii 
performance and that business-cycle rotation strategies generate excess market 
performance. The study tests these two fundamental assumptions of popular rotation 
strategies. Initially, the study assumes investors can perfectly anticipate business cycles 
and implement conventional sector rotation. However, there is no evidence of 
systematic sector performance where popular belief anticipates it will occur. At best, 
conventional sector rotation generates 2.3 percent annual excess returns. This 
performance quickly diminishes after an allowance for transaction costs and incorrectly 
timing business cycles. An examination of all sectors across all business-cycle stages 
produces evidence of in-sample systematic sector performance, but an out-of-sample 
alternative rotation strategy fails to generate excess performance. Overall, the study 
documents unsystematic sector performance across business cycles, questioning the 
popularity of sector rotation as a viable investment strategy. 
  
 iii 
Acknowledgements 
Embarking on a PhD is not so much about the destination as the journey. Most 
importantly, it is about the people you encounter on the way. Many people helped me 
on my journey. I would like to thank them now. My greatest appreciation goes to my 
supervisors, Associate Professor John Pinfold and Professor Martin Young. Thank you 
for your faith. I am especially indebted to Professor Larry Rose. Your door was always 
open. Heartfelt thanks also go to Professor Henk Berkman. Your friendship and 
inspiration guided this journey, more than you know. Special thanks go to Dr. Peren 
Arin, Catherine Beaumont, Mary Dawkins, Sharon Henderson, Rochelle Hughes, Liz 
Stewart, and Dr. Bill Wilson, for being friends. 
I dedicate this PhD to the spirit of Nabelle. Isabelle and Nathan, this journey will 
define your childhood memories. Thanks for understanding all of those missed 
weekends and summer vacations. To Loren, my wife, you are the guardian of the spirit. 
We did this together, as everything. The journey would not have been bearable without 
you. Lastly, to Liz, my mother, the spirit of Nabelle emanates from you. You are life’s 
ultimate traveler and made this journey possible, in so many ways. 
  
 iv 
Table of Contents 
Abstract .................................................................................................................... i 
Acknowledgements ................................................................................................ iii 
Table of Tables ..................................................................................................... viii 
Chapter 1 Introduction .............................................................................................. 1 
1.1 Introduction ...................................................................................................... 1 
1.2 General Literature Overview .......................................................................... 3 
1.2.1 Behavioral finance and style investing .................................................................................. 3 
1.2.2 Sentiment cycles ..................................................................................................................... 4 
1.2.3 Political cycles ........................................................................................................................ 5 
1.2.4 Business cycles ....................................................................................................................... 6 
1.2.5 Industry-level research ........................................................................................................... 7 
1.3 Contribution to the Literature ........................................................................ 8 
1.3.1 Investor sentiment and industry returns ................................................................................. 8 
1.3.2 Political cycles in U.S. industry returns ............................................................................... 11 
1.3.3 Sector rotation across business cycles .................................................................................. 12 
1.4 Organization of Dissertation ......................................................................... 14 
Chapter 2 Investor Sentiment and Industry Returns ........................................... 15 
2.1 Introduction .................................................................................................... 15 
2.2 Literature Review and Hypotheses Development ....................................... 19 
2.3 Investor Sentiment Measures and Market Return Data ............................ 23 
 v 
2.3.1 Sentiment measures .............................................................................................................. 24 
2.3.2 Market data ........................................................................................................................... 28 
2.4 Markets and Sentiment .................................................................................. 29 
2.5 Industries and Sentiment ............................................................................... 31 
2.5.1 Description of industry data ................................................................................................. 32 
2.5.2 Basic industry regressions .................................................................................................... 35 
2.5.3 Economic significance of investor sentiment predictability ................................................ 37 
2.5.4 Bullish and bearish investor sentiment ................................................................................ 38 
2.6 Control for Conditional Time-variant Market-risk Premium ................... 43 
2.7 Interaction of Industry Characteristics with Investor Sentiment .............. 46 
2.7.1 Industry characteristics ......................................................................................................... 47 
2.7.2 Non-parametric ranking of industry performance ............................................................... 49 
2.7.3 Interaction between investor sentiment and industry characteristics .................................. 51 
2.7.4 Regression of industry sentiment betas on industry characteristics .................................... 54 
2.7.5 Regression of industry characteristics on investor sentiment .............................................. 56 
2.7.6 Fama and MacBeth regressions for industry characteristics ............................................... 57 
2.8 Investor Sentiment Strategy Returns ........................................................... 59 
2.9 Event Study with Extreme Bullish and Bearish Sentiment ........................ 65 
2.10 Robustness Checks ....................................................................................... 68 
2.10.1 Sub-period comparison ....................................................................................................... 69 
2.10.2 Controls for risk factors ...................................................................................................... 71 
 vi 
2.10.3 Alternative industry portfolios ........................................................................................... 75 
2.11 Conclusion ..................................................................................................... 78 
Chapter 3 Political Cycles in U.S. Industry Returns ............................................. 80 
3.1 Introduction .................................................................................................... 80 
3.2 Literature Review and Hypotheses Development ....................................... 83 
3.3 Results for the General Market .................................................................... 86 
3.4 Industry Presidential Cycles .......................................................................... 93 
3.5 Industry Quadrennial Cycles ...................................................................... 101 
3.6 Event Study Results ...................................................................................... 107 
3.7 Conclusion ..................................................................................................... 122 
Chapter 4 Sector Rotation across the Business Cycle ......................................... 124 
4.1 Introduction .................................................................................................. 124 
4.2 Literature Review and Hypotheses Development ..................................... 130 
4.3 Business Cycles ............................................................................................. 134 
4.3.1 NBER business cycle dates ................................................................................................ 134 
4.3.2 Business cycle stages .......................................................................................................... 136 
4.3.3 Evaluation of business cycles proxies ................................................................................ 137 
4.4 Industry Performance across Business Cycles .......................................... 140 
4.4.1 Data description .................................................................................................................. 140 
4.4.2 Popular guidance on industry performance ....................................................................... 141 
4.4.3 Nominal industry performance ........................................................................................... 141 
 vii 
4.4.4 Risk-adjusted industry performance measures .................................................................. 145 
4.5 Sector Rotation Performance ...................................................................... 149 
4.6 Robustness Checks ....................................................................................... 152 
4.6.1 Alternative sector/industry groups and data sets ............................................................... 152 
4.6.2 Alternative business-cycle stage delineation ..................................................................... 157 
4.6.3 Different ways to measure the business cycle ................................................................... 161 
4.6.4 Timing the business cycle in advance or with a delay ....................................................... 167 
4.6.5 Different business-cycle proxies ........................................................................................ 168 
4.6.6 Description of other robustness tests .................................................................................. 173 
4.7 General Sector Performance across the Business Cycle ........................... 176 
4.7.1 Relative industry performance rankings ............................................................................ 177 
4.7.2 Percentage of months with excess market performance .................................................... 179 
4.7.3 Alternative sector rotation strategy .................................................................................... 182 
4.8 Conclusion ..................................................................................................... 186 
Chapter 5 Conclusions ........................................................................................... 187 
5.1 Summary of Contributions .......................................................................... 187 
5.1.1 Investor sentiment and industry returns ............................................................................. 188 
5.1.2 Political cycles in U.S. industry returns ............................................................................. 189 
5.1.3 Sector rotation across business cycles ................................................................................ 190 
5.2 Limitations and Future Research Agenda ................................................. 191 
References ............................................................................................................... 194 
 viii 
Table of Tables 
Table 2.1 Descriptive statistics for investor sentiment and market indices ............... 27 
Table 2.2 Investor sentiment predictability of index returns ...................................... 30 
Table 2.3 Industry descriptive statistics ..................................................................... 34 
Table 2.4 Investor sentiment predictability of industry returns ................................. 36 
Table 2.5 Economic significance of investor sentiment predictability ...................... 38 
Table 2.6 Investor sentiment predictability during bull markets ................................ 42 
Table 2.7 Investor sentiment predictability during bear markets ............................... 43 
Table 2.8 Interaction between investor sentiment and the market ............................. 45 
Table 2.9 Performance ranking by industry characteristic ......................................... 51 
Table 2.10 Interaction between investor sentiment and industry characteristics ....... 53 
Table 2.11 Regressions for industry sentiment betas ................................................. 55 
Table 2.12 Regressions for industry characteristics ................................................... 57 
Table 2.13 Fama and MacBeth coefficients for industry characteristics ................... 59 
Table 2.14 Investor sentiment strategy performance ................................................. 61 
Table 2.15 Sub-period strategy performance ............................................................. 64 
Table 2.16 Event study results for extreme investor sentiment ................................. 67 
Table 2.17 Sub-period comparison of investor sentiment predictability ................... 70 
Table 2.18 Risk adjusted comparison of investor sentiment predictability ............... 73 
Table 2.19 Four-factor risk adjusted investor sentiment predictability ...................... 74 
Table 2.20 Alternative sector and industry mappings ................................................ 76 
Table 2.21 Investor sentiment predictability using alternative industries .................. 78 
Table 3.1 Market descriptive statistics for presidential and quadrennial cycles ........ 88 
Table 3.2 Industry summary statistics for presidential and quadrennial cycles ......... 94 
Table 3.3 Industry presidential cycles with a market correction ................................ 98 
 ix 
Table 3.4 Industry presidential cycles with a three-factor correction ........................ 99 
Table 3.5 Presidential cycle summary for different models and sub-periods .......... 100 
Table 3.6 Industry quadrennial cycles with a market correction ............................. 104 
Table 3.7 Industry quadrennial cycles with a three-factor correction ...................... 105 
Table 3.8 Quadrennial cycle summary for different models and sub-periods ......... 106 
Table 3.9 Poll results for the 2004 presidential election .......................................... 109 
Table 3.10 Event study groupings for the 2004 presidential election ...................... 113 
Table 3.11 CAARs for industry groups .................................................................... 116 
Table 4.1 Business cycle stages of expected industry performance ......................... 129 
Table 4.2 NBER reference business cycle dates and stage partitions ...................... 136 
Table 4.3 Business cycle proxies across business cycle stages ................................ 139 
Table 4.4 Industry summary statistics by business-cycle stages .............................. 143 
Table 4.5 Industry performance measures by business-cycle stages ....................... 146 
Table 4.6 Performance comparison of alternative investment strategies ................. 150 
Table 4.7 Alternative sector and industry mappings ................................................ 153 
Table 4.8 Summary statistics for alternative sector and industry groups ................. 155 
Table 4.9 Two-stage industry summary statistics and performance ........................ 159 
Table 4.10 Four-stage industry summary statistics and performance ...................... 160 
Table 4.11 Industry performance over CFNAI business-cycle stages ..................... 164 
Table 4.12 Industry performance sensitivity to CFNAI business cycles ................. 166 
Table 4.13 Comparison of strategy performance with different timing ................... 168 
Table 4.14 Forecast model sector rotation ............................................................... 171 
Table 4.15 Average ranking of excess market industry performance ...................... 178 
Table 4.16 Percentage of months realized industry returns exceed the market ....... 181 
Table 4.17 Alternative sector rotation strategy ........................................................ 185 
 x 
Table of Figures 
Figure 2.1 Investor sentiment cycles .......................................................................... 26 
Figure 2.2 Time-variant industry sentiment betas ...................................................... 39 
Figure 3.1 Value-weighted market returns by U.S. presidential administration ........ 84 
Figure 3.2 Industry returns over presidential cycles .................................................. 96 
Figure 3.3 Industry quadrennial cycles .................................................................... 102 
Figure 3.4 Election probabilities for the 2004 presidential election ........................ 110 
Figure 3.5 CAARs based on past industry performance .......................................... 118 
Figure 3.6 CAARs based on analyst recommendations and contributions .............. 120 
Figure 3.7 Cumulative CRSP index returns for the 2004 presidential election ....... 121 
Figure 4.1 Popular guidance on sector rotation ........................................................ 128 
Figure 4.2 Stylized business cycles with stage partitions ........................................ 137 
Figure 4.3 CFNAI delineated business-cycle stages ................................................ 163 
Figure 4.4 Predictability of excess market industry performance ............................ 174 
Figure 4.5 Performance ranking for the computer hardware industry ..................... 179 
Figure 4.6 Distribution of excess market and Jensen’s alpha performance ............. 183 
 
 1 
Chapter 1 Introduction 
1.1 Introduction 
The efficient market hypothesis is the basis of modern financial theory. In a seminal 
study, Fama (1970, pg. 383) states, “A market in which prices always ‘fully reflect’ 
available information is called ‘efficient’.” Stock prices should follow a random walk, 
without predictable patterns, due to the unpredictable arrival of price-relevant 
information. Fama (1970) describes weak, semi-strong and strong form market 
efficiency. Weak form efficiency requires that there are no predictable patterns in 
historical stock prices. Semi-strong form efficiency requires that prices immediately 
reflect all publicly available information affecting fundamental values. Additionally, 
strong form efficiency requires that prices reflect all publicly and privately available 
information. The empirical evidence, generally, supports weak and semi-strong form 
efficiency, but not strong form efficiency. The efficient market hypothesis allows for the 
possibility that individual investors may overreact or underreact to stock information. 
However, market prices should quickly adjust to reflect rational expectations and 
fundamental values. In efficient markets, there should be no opportunity for investors to 
formulate trading strategies that generate systematic outperformance (Malkiel (2003)). 
Nonetheless, empirical evidence of market cycles and the popularity of trading 
strategies based on predictable market cycles appear to contradict the efficient market 
hypothesis. 
Behavioral finance theory provides an alternative to the efficient market hypothesis, 
describing mechanisms that allow for price divergence from fundamental values and 
predictable patterns in stock returns. An important survey by Shiller (2003, pg. 83) 
describes behavioral finance as “vital research [that] stands in sharp contradiction to 
much of efficient markets theory.” His survey reviews a litany of market anomalies 
unexplained by rational expectations. Shiller (2003) argues that irrational investor 
exuberance leads to speculative cycles and, ultimately, asset pricing bubbles. Shiller 
(2003) also observes that speculative pricing, unrelated to fundamentals, commonly 
occurs in certain investment “styles.” Take industry styles, for example. In a key paper, 
Barberis and Shleifer (2003) formalize a model where investor cognitive limitations 
lead to style investing and temporary mispricing. Teo and Woo (2004), among others, 
 2 
provide empirical evidence of excess style performance unrelated to fundamentals and 
rational expectations. A number of important studies, such as Hong, Torous, and 
Valkanov (2007) and Santa-Clara and Valkanov (2003), document predictable cycles in 
asset returns unexplained by fundamental values that are in violation of market 
efficiency. The behavioral finance literature thus suggests predictable industry cycles 
and profitable investment strategies, contrary to market efficiency. 
Popular investor belief holds that predictable market cycles allow for profitable 
investment strategies. Common cyclical investment strategies relate to seasonal cycles, 
political election cycles, or business cycles. Kamstra, Kramer, and Levi (2003), for 
instance, document a seasonal affective disorder (SAD) in stocks, which translates into 
seasonal stock market patterns driven by investor sentiment. The financial press also 
widely reports on investor-sentiment cycles as predictors of market performance. A 
number of studies, for instance Santa-Clara and Valkanov (2003) and Snowberg, 
Wolfers, and Zitzewitz (2007), confirm political cycles in market index returns. 
Business cycles also provide the basis for different investment strategies. Siegel (2002) 
describes a profitable investment strategy that rotates between equities and cash at 
business cycle turning points. Sector rotation is another widely followed investment 
strategy, which rotates sector and industry investments with business-cycle stages. 
Other stock market cycles relate to the month of the year; Federal Reserve Bank 
monetary policy; sporting events; and credit expansion and contraction. This 
dissertation examines industry performance for evidence of sentiment cycles, political 
cycles, and business cycles. 
The empirical studies that comprise this dissertation investigate industry cycles and 
investor sentiment, contributing to the asset pricing, market efficiency, and investment 
strategy literatures. The first study explores the interaction between investor-sentiment 
cycles and industry performance. The second study examines industry returns for 
evidence of political cycles. The third study analyzes industry performance over 
business cycles. That study also investigates whether a popular investment strategy, 
known as sector rotation, generates excess returns. Any evidence of systematic industry 
cycles is inconsistent with the basic tenets of market efficiency. Nonetheless, investors 
popularly believe that trading on different industry cycles systematically generates 
excess returns. The popularity of industry investing is evident from the capital flows it 
 3 
attracts. A rigorous examination of cyclical industry performance contributes to the 
literature from both theoretical and practical perspectives, providing new insights into 
stock market cycles and industry investing. 
Prior to discussing the research questions addressed by the empirical studies, the next 
section reviews background literature. 
1.2 General Literature Overview 
This section provides an extended discussion of key literature related to the dissertation. 
Section 1.2.1 discusses influential literature on style investing theories, which support 
the idea of industry style cycles and viable industry rotation strategies. Sections 1.2.2 to 
1.2.4 briefly discuss key literature on stock-market cycles. The concluding section 
discusses literature on industry-style investing. 
1.2.1 Behavioral finance and style investing 
This dissertation examines the relationship between industry performance and investor 
sentiment, presidential election cycles and business cycles. Recurring cycles such as 
these should have no effect on expected industry returns. A basic tenet of the efficient 
market hypothesis states that asset prices follow a random walk, without predictable 
patterns. Nonetheless, popular belief suggests that predictable cycles lead to profitable 
trading strategies. Behavioral finance provides an alternative theory on style investing, 
which results in predictable cycles and mispricing. 
In a seminal paper, Barberis and Shleifer (2003) formulate a behavioral model of 
style investing. The model maintains that investors make investment decisions on styles 
rather than stock fundamentals. Barberis and Shleifer (2003) base their model on a 
simple premise: humans naturally categorize to simplify otherwise complex analysis. 
The theory also argues that individual traders invest in styles due to their limitations in 
processing information on individual stocks. Barberis and Shleifer (2003) further 
suggest that institutional investors benefit from style categorization, which allows 
performance evaluation and adherence to fiduciary guidelines. Some common 
investment styles are, for example, small-cap stocks, large-cap stocks, value stocks, 
growth stocks, and industry groups. The Barberis and Shleifer (2003) style model makes 
two basic assumptions. The first is that investors base allocation decisions on past style 
 4 
performance, rotating into good-performing styles and rotating out of bad-performing 
styles. The second assumption is that style investing affects prices. Barberis and Shleifer 
(2003) theorize that style investing results in the correlated returns of stocks that share a 
style classification, independent of fundamental stock values. The style model holds that 
style values deviate from fundamental value as their popularity ebbs and flows, 
resulting in short-term mispricing and long-term reversals. 
A study by Peng and Xiong (2006) extends the Barberis and Shleifer (2003) style-
investing model specifically to industry styles. Peng and Xiong (2006) also base their 
model on limited investor attention, which leads to category learning. Attention deficits 
cause investors to focus their attention on market- and industry-level information rather 
than firm-level information. The Peng and Xiong (2006) model additionally states that 
investor overconfidence in their ability to process industry information results in 
overreaction to news and biased price valuations. The model predicts that a combination 
of investor category learning and overconfidence ultimately results in industry return 
correlations in excess of fundamental values. Moreover, Peng and Xiong (2006) predict 
that differences in industry return correlations relate to the informational content of 
industry-constituent stocks. Industries with less informative prices exhibit greater return 
correlations and related mispricing. Conversely, the model predicts that investors 
allocate more attention to firm-specific information for concentrated industries where 
stock prices are more informative. As such, the stocks in those industries are less subject 
to predictable investor overreaction. Peng and Xiong (2006, pg. 566) conclude that the 
attention-allocation decisions of investors ultimately result in predictable cycles in 
industry returns. 
The next three sections briefly discuss sentiment cycles, political cycles, and 
business cycles, investigated by the three empirical studies comprising this dissertation. 
1.2.2 Sentiment cycles  
At times, investor sentiment seemingly drives stock market values. Baker and Wurgler 
(2006) summarize well-known stock market cycles, such as the 1980s biotechnology 
and 1990s Internet bubbles, that coincide with cycles of investor sentiment. High 
investor sentiment typifies periods of speculative market values and low investor 
sentiment typifies the opposite. In either instance, market values can appear inconsistent 
 5 
with market efficiency and rational expectations. A growing literature, nevertheless, 
discusses how investor sentiment affects asset values and provides empirical evidence 
that investor sentiment predicts prices that deviate from fundamental values. 
An empirical study by Brown and Cliff (2005) explores the ability of investor 
sentiment to predict market-pricing errors. Brown and Cliff (2005) find that excessively 
positive (negative) investor sentiment initially drives market prices above (below) 
fundamental value. Investor sentiment predicts market reversals at one- to three-year 
horizons, which are economically significant. However, Brown and Cliff (2005) 
question the practicality of trading on investor-sentiment strategies, given the potential 
for protracted periods of market mispricing. Nonetheless, Brown and Cliff (2005) 
confirm that investor sentiment predictably affects asset prices, in contravention of 
market efficiency. 
Baker and Wurgler (2006) investigate the interaction between investor sentiment and 
stocks grouped by common characteristics. The central finding of Baker and Wurgler 
(2006) is that cross-sectional differences in the effect of investor sentiment on stock 
values relate to speculative demand and limits on arbitrage. Baker and Wurgler (2006) 
provide evidence that difficult-to-value stocks are especially subject to speculative 
investor sentiment. Difficult-to-value stocks share certain characteristics, described by 
profitability, return momentum, overall risk, dividend policy, asset tangibility, growth 
opportunities, and financial distress. Baker and Wurgler (2006) argue that it is also 
difficult to arbitrage away mispricing with stocks sharing those same attributes. Baker 
and Wurgler (2006) provide evidence that investor sentiment leads to predictable 
patterns of mispricing and subsequent price reversals, which traditional rational-
expectations arguments fail to explain. 
1.2.3 Political cycles 
Political stock market cycles center on the four-year term of office for a U.S. president. 
Investors popularly believe that presidential politics affect the performance of financial 
markets. The Stock Trader’s Almanac, published by Yale Hirsch, originally popularized 
the “Presidential Election Cycle” trading strategy. Conventional wisdom maintains that 
rotating investments based on the outcomes of presidential elections generates market 
outperformance. In particular, investors believe that whether a president is a Democrat 
 6 
or a Republican determines the performance of particular industries. Based on the 
efficient market hypothesis, presidential politics should have no predictable effect on 
stock prices. 
Extant literature documents political stock market cycles related to a president’s 
political affiliation and the year of a presidential term of office. The premise of 
presidential election cycles is that politicians formulate policies that benefit groups with 
shared ideologies.1 The market perceives the Republican Party as favoring business and 
the Democratic Party as favoring labor (Hibbs (1977)). However, empirical research, 
such as Herbst and Slinkman (1984) and Santa-Clara and Valkanov (2003), finds that 
the stock market actually performs best under Democrats. Quadrennial cycles depend on 
the year of a presidential term of office.2 The basis of quadrennial cycles is the belief 
that politicians manipulate the economy in the second half of their term for reelection 
benefit. A seminal study by Nordhaus (1975) provides a theoretical model of 
quadrennial election cycles. Nordhaus (1975) theorizes that politicians manipulate the 
economy to gain re-election. The Nordhaus (1975) model stipulates a cyclical trade-off 
between inflation and employment, where the economy oscillates between austerity and 
excess over an incumbent president’s term.  
1.2.4 Business cycles 
The National Bureau of Economic Research (NBER) defines business cycles by phases 
of economic expansion and economic contraction. Investors widely believe that 
business cycles drive asset returns. A popular investment strategy believed to generate 
excess returns, known as sector rotation, invests in particular sectors or industries across 
different stages of the business cycles. Systematic strategy performance timing industry 
investments with the business cycle is inconsistent with market efficiency. Nonetheless, 
a nascent literature documents predictable industry performance and exploitable 
investment strategies. 
                                                 
1 The literature also refers to presidential election cycles as partisan election cycles.  
2 The literature also refers to quadrennial cycles as opportunistic cycles 
 7 
Recent research provides some evidence that the trading patterns of investors in 
industry portfolios do track business cycles. Beber, Brandt, and Kavajecz (2010) 
investigate the active order flows of institutional investors using Trades and Quotes 
(TAQ) data from 1993 to 2005. Their study maps each stock in the Center for Research 
on Stock Prices (CRSP) database to one of 10 Global Industry Classification Standard 
(GICS) sectors. Beber, Brandt, and Kavajecz (2010) define active order flows as the 
difference between total sector order flows and sector order flows relative to their total 
market capitalization. Beber, Brandt, and Kavajecz (2010) speculate that institutional 
investors base their trades on macroeconomic news. The primary finding of Beber, 
Brandt, and Kavajecz (2010) is that the active trade flows of institutional investors 
predict macroeconomic conditions. Moreover, Beber, Brandt, and Kavajecz (2010) 
observe that the portfolio rebalancing of institutional investors emulates a conventional 
sector rotation strategy. As such, institutional investors appear to base their expectations 
for sector performance on business-cycle fluctuations. Further, Beber, Brandt, and 
Kavajecz (2010) conclude that translating the active order rebalancing of institutional 
investors into a sector rotation strategy results in excess performance. 
The concluding section discusses the importance of industry-level research. 
1.2.5 Industry-level research 
Additional insight into industry returns is important from both academic and practical 
perspectives. An influential study by Moskowitz and Grinblatt (1999, pg. 1251) 
recognizes “an important role for industries in understanding financial markets.” 
Moskowitz and Grinblatt (1999) investigate the contribution of industries to individual 
stock momentum strategies over the period 1963 to 1995. Their study finds that industry 
performance largely explains previously documented momentum profits. Moreover, 
Moskowitz and Grinblatt (1999) speculate that investor herding in “hot” industries 
creates speculative price pressure and excess returns. Similarly, Jame and Tong (2009) 
attribute the poor performance of individual investors documented by Barber, Odean, 
and Zhu (2009), among others, to industry herding. Jame and Tong (2009) examine 
investor trade imbalances, using the Trade and Quote (TAQ) database, over the sample 
period 1983 to 2000. Their results show industry selection accounts for 60 percent of 
the portfolio performance of individual investors. Studies such as Moskowitz and 
 8 
Grinblatt (1999) and Jame and Tong (2009) confirm the Barberis and Shleifer (2003) 
style model, which predicts short-term style momentum and long-term reversals. 
Insight into industry performance has important practical implications. Industry 
mutual and exchange-traded funds are important growth segments for equities markets.3 
Cavaglia, Brightman, and Aked (2000) and Baca, Garbe, and Weiss (2000) show that 
industry investing provides greater diversification than country investing. More 
recently, Phylaktis and Xia (2006) report on the increasing importance of industry 
diversification since 1999. Studies also document the value of industry investing for 
portfolio returns. Kacperczyk, Sialm, and Zheng (2005) investigate the performance of 
mutual fund managers, using data from 1984 to 1999. Their study documents that 
targeted industry investments generate fund outperformance. Rapach, Strauss, Tu, and 
Zhou (2010), with data from 1946 to 2008, provide evidence that common economic 
variables predict widespread industry performance. Their study reports different levels 
of industry predictability, which could allow profitable industry rotation. Nonetheless, 
Rapach, Strauss, Tu, and Zhou (2010) note the lack of empirical research on industry 
investment performance, a gap this dissertation bridges. 
1.3 Contribution to the Literature 
This section reviews the research questions addressed by this dissertation’s three 
empirical studies and contributions to the literature. The main contribution is a 
systematic investigation of industry returns for evidence of cyclical performance 
previously documented in the general market and the basis of popular investment 
strategies. The three cycles investigated are investor-sentiment cycles, political cycles, 
and business cycles. 
1.3.1 Investor sentiment and industry returns 
The first study, “Investor Sentiment and Industry Returns,” examines the interaction 
between investor-sentiment measures and industry performance. The financial media 
widely report on different measures of investor sentiment as predictors of market 
values. Yet market efficiency states that market values reflect rational expectations, 
                                                 
3 Investment Company Institute (2010) 
 9 
leaving no role for investor sentiment in asset pricing. Baker and Wurgler (2006) 
provide anecdotal evidence that implies otherwise. Baker and Wurgler (2006) show a 
correlation between stock market cycles and investor sentiment. The performance of 
particular industries appears especially affected by investor sentiment. A nascent 
empirical literature documents evidence of market mispricing related to investor 
sentiment. Given the implication for market efficiency, understanding how investor 
sentiment affects industry returns provides a worthwhile research topic, from theoretical 
and practical perspectives. Firstly, cross-sectional differences in the effect of sentiment 
on industry performance can potentially explain the market-wide effect of investor 
sentiment. Secondly, industry groups denote important investment styles susceptible to 
speculative investor sentiment. As such, understanding the effect of investor sentiment 
on industry performance also has practical implications for industry investment 
strategies. 
While several empirical studies document investor sentiment predictability of market 
returns, none document industry return predictability. Fisher and Statman (2000), 
Brown and Cliff (2005), and Baker and Wurgler (2006) provide evidence that investors’ 
sentiment causes short-term mispricing, followed by predictable long-term price 
reversal to fundamentals. The literature, such as Kumar (2009) and Barber, Odean, and 
Zhu (2009), further documents that mispricing results from the collective trades of 
uninformed investors, who base decisions on sentiment rather than fundamental values. 
A related study by Kumar (2009) reports that individual investors shift preferences 
between style extremes, such as growth and value stocks, which leads to mispricing. 
Another related study by Jame and Tong (2009) documents that speculative pricing 
results when investors herd together in industry styles. Baker and Wurgler (2006) report 
a correlation between speculative investor sentiment and stock characteristics that make 
objective valuations difficult. Similar to Baker and Wurgler (2006), the first study also 
examines the relationship between sentiment and common industry characteristics that 
potentially attract investor speculation. While empirical studies document sentiment-
driven mispricing, the literature largely ignores the effect of investor sentiment on 
industry performance. 
The first study’s main contribution is a rigorous examination of how investor 
sentiment affects industry performance. Such an examination is important from 
 10 
different perspectives. Firstly, prior research documents that investor sentiment predicts 
market returns. Return predictability violates market efficiency, which states that asset 
values reflect fundamentals and rational expectations. However, prior research by 
Moskowitz and Grinblatt (1999) documents a link between efficient market violations 
and industry returns. A thorough investigation of the interaction between investor 
sentiment and industry returns could thus explain the market-wide effect of investor 
sentiment. Secondly, the first study contributes to the literature that predicts investor 
sentiment leads to mispricing in investment styles, such as industry categories. Lastly, 
popular belief holds that investor sentiment determines industry values. Industry 
investing is also a growing segment of the equities market. Thus, examining the value of 
investor sentiment in industry rotation strategies has important practical implications. 
The first study provides evidence that investor sentiment predicts industry returns. 
An industry rotation strategy that times investor sentiment, however, generates only 
marginal outperformance. The study investigates the predictability of industry returns 
using three sentiment measures: the American Association of Independent Investors 
survey, the Investors Intelligence survey and the Baker and Wurgler (2006) sentiment 
index. The sample period runs from 1967 to 2007, which corresponds with the 
availability of investor sentiment data. Confirming prior market studies, the results 
show short-term mispricing and long-term reversals, especially in equal-weighted 
market indices. Investor sentiment predicts a similar pattern of industry returns, almost 
universally. Negative predictability of long-term industry reversals is, for the full 
sample, much weaker than in previous market studies. Notably, long-term industry 
reversals are greater during bear markets. An interpretation is that industry mispricing 
takes longer to correct in bull markets, due to short-sale restrictions. There is no 
evidence that industry characteristics, which serve as a proxy for valuation difficulty, 
attract speculative mispricing. Lastly, rotating industries with investor sentiment 
generates 3 to 6 percent outperformance, before high turnover costs. The results 
document that investor sentiment has a market-wide rather than industry-specific effect 
on return predictability, questioning the practical application of investor-sentiment 
rotation strategies. 
 11 
1.3.2 Political cycles in U.S. industry returns 
The second study, “Political Cycles in U.S. Industry Returns,” investigates industry 
returns for evidence of presidential election cycles. The literature documents political 
stock market cycles related to a president’s political party (presidential cycle) and the 
year of a presidential term (quadrennial cycle). Popular belief also suggests a link 
between industry performance and political outcomes. An investigation of industry 
political cycles is important for two different reasons. Firstly, political cycles in the 
general market violate market efficiency, which industry analysis potentially explains. 
Secondly, investigating political cycles in industry returns is interesting from a practical 
perspective, given popular belief that presidential election outcomes determine industry 
performance. 
The second study extends the empirical research of Santa-Clara and Valkanov 
(2003). The Santa-Clara and Valkanov (2003) study confirms market outperformance 
during Democratic presidencies relative to Republican ones. The difference in excess 
market performance between Democratic and Republican presidencies ranges from 9 
percent to 16 percent for value-weighted and equal-weighted indices. Santa-Clara and 
Valkanov (2003) also examine the performance differential for size-deciles formed on 
all U.S. listed equities. They find that Democratic outperformance increases 
monotonically from 7 percent to 22 percent from the largest to the smallest firms. Santa-
Clara and Valkanov (2003) determine that the Democratic return premium 
systematically surprises investors each election. Santa-Clara and Valkanov examine 
explanations of the Democratic premium within a rational-expectations framework. 
However, counter to a risk-based explanation, market volatility is less during 
Democratic presidencies. Santa-Clara and Valkanov (2003) show that presidential 
cycles are not a proxy for business cycles, concluding that presidential election cycles 
represent an unresolved financial market puzzle. 
The main contribution of the second study is an investigation of industry returns for 
evidence of presidential election cycles, as a possible explanation for the political stock 
market cycles that the literature documents. A few dominant industries, especially 
sensitive to political outcomes, could explain political cycles attributed to the general 
market. Popular belief holds, for instance, that the defense and pharmaceutical 
 12 
industries perform best when a Republican is president. Additionally, Santa-Clara and 
Valkanov (2003) establish that political stock market cycles comprise unexpected and 
expected elements. As a result, the second study provides a thorough examination of 
industry performance over time and in response to election outcomes, in order to 
understand the dynamics of political stock market cycles. 
The second study documents an absence of systematic political cycles in industry 
returns. The study examines market returns over 21 presidential election cycles from 
1926 to 2006. Market and industry performance is systematically higher under 
Democratic presidents and during the last two years of any four-year presidential term. 
However, with a Fama and French three-factor risk correction, industry returns show no 
evidence of systematic performance related to presidential election cycles. Additionally, 
there is only scant evidence of abnormal industry performance in response to the 2004 
presidential election. By implication, it appears that investors do not take election 
outcomes as a reliable signal of expected industry performance. Generally, presidential 
election cycles appear restricted to the general market. The results suggest a 
macroeconomic explanation of the presidential election puzzle, questioning the popular 
belief that politics drives industry performance. 
1.3.3 Sector rotation across business cycles 
The third study, “Sector Rotation over Business Cycles,” investigates a popular 
investment strategy that rotates sector allocations with business cycles. Sector rotation 
strategies play a key role in equity fund management (Fabozzi (2007)). Capital inflows 
into sector and industry funds emphasize the popularity of sector rotation with investors. 
Profitable sector rotation presumes an ability to predict business-cycle stages and select 
sectors that perform well during these stages. Despite the significant capital that sector 
rotation attracts, these basic presumptions run counter to the efficient market 
hypothesis. Sector returns should follow a random walk rather than exhibit systematic 
patterns that correspond with business cycles. The popularity of sector rotation is 
interesting from two perspectives. Evidence of systematic sector performance across 
business cycles would present a new financial market anomaly. Alternatively, a lack of 
systematic sector performance would question the popular belief that timing sector 
investments with business cycles generates excess market performance. 
 13 
Despite the popularity of sector rotation, the literature has not directly tested sector 
performance and strategy performance over business cycles. There are a number of 
related studies. Levis and Liodakis (1999) and Ahmed, Lockwood, and Nanda (2002) 
report significant strategy outperformance using business-cycle variables to rotate 
stocks based on accounting characteristics. DeStefano (2004) examines general stock 
market performance over four NBER-delineated business-cycle stages. DeStefano 
documents strong evidence of market performance related to business cycle patterns. In 
a closely related study, Conover, Jensen, Johnson, and Mercer (2008) rotate cyclical 
stocks and non-cyclical stocks over stages of monetary expansion and contraction, 
generating systematic strategy outperformance. Investors, however, typically divide 
business cycles into more discrete stages. They also more commonly think in terms of 
the performance of industry styles over business cycles (Bodie, Kane, and Marcus 
(2009) and Lofthouse (2001)). The third study tests industry performance over multiple 
business-cycle stages, to align the results with conventional sector rotation practice. 
The main contribution of the third study is its original and rigorous examination of 
the basic assumptions of sector rotation. Those assumptions are systematic sector 
performance that allows the implementation of profitable investment strategies. The 
difficulty testing sector rotation is correctly timing business cycle stages. The third 
study takes a new approach to overcome that obstacle, assuming that investors can 
correctly pick business cycle stages to time their sector investments. The study initially 
assumes a rotation strategy that follows popular guidance on sector performance. 
Allowing for possible variants of sector rotation, the third study further tests the 
performance of all industries across all business cycle stages. 
The third study documents unsystematic industry performance across business cycles 
and questions the practicality of popular sector rotation strategies. The base-case sample 
covers 10 complete NBER business cycles from 1948 to 2007. Three stages of 
expansion and two of recession delineate each NBER business cycle. Industries do not 
generate systematic performance, where popular belief anticipates it or, more generally, 
anywhere along the business cycle. A popular sector rotation strategy that times 
industry investments with business cycle stages generates marginal outperformance, 
even before transaction costs. A battery of robustness tests, which use different industry 
classifications, samples periods, business cycle measures and risk adjustments, confirm 
 14 
the base-case results. The results generally confirm that no variant of sector rotation 
generates outperformance. While this finding confirms market efficiency, it questions 
the validity of sector rotation. 
1.4 Organization of Dissertation 
The remaining chapters of the dissertation are as follows: Chapter 2 examines investor 
sentiment predictability of industry returns and the practical usefulness of investor-
sentiment measures in industry rotation strategies. Chapter 3 explores the relationship 
between U.S. presidential cycles and industry performance. Chapter 4 investigates 
industry performance across business cycles and evaluates the profitability of a popular 
rotation strategy. Lastly, Chapter 5 concludes with a summary of the dissertation’s main 
contributions and outlines a follow-up agenda for future research. 
 
 15 
Chapter 2  Investor Sentiment and Industry Returns 
2.1 Introduction 
Traditional financial theory does not allow a role for investor sentiment in asset pricing. 
Its principal tenet, the efficient market hypothesis, maintains that rational investors price 
assets solely on discounted future cash flows. Moreover, traditional theory further posits 
that stock prices will immediately reflect fundamental news. If temporary stock 
mispricing occurs, market efficiency suggests that arbitrageurs quickly restore stock 
prices to fair value.4 Yet casual observation suggests that irrational investor behavior 
periodically drives prices from fundamentals over protracted periods. NASDAQ stock 
valuations in the 1990s illustrate one well-documented example. Other examples of 
stock market cycles further suggest that stock prices periodically reflect investor 
sentiment and that prices revert to fair value only with a delay.5 
An evolving body of literature, coupled with practitioner interest in the topic, 
provides evidence that investor sentiment, in some part, affects stock valuations. Brown 
and Cliff (2005), for instance, document a statistically and economically significant 
relationship between investor sentiment and the market, where index values initially 
overshoot and then revert, with a delay, to fundamental value. Baker and Wurgler 
(2006) argue that mispricing is prevalent in stocks that are difficult to objectively value. 
For instance, stocks in an emerging industry, which characteristically have uncertain 
future earnings, are especially subject to speculative investor valuations. Additionally, 
the financial media (such as Barron’s, Wall Street Journal, Forbes, and CNBC) 
regularly report on different market sentiment measures.6 John Hancock Financial, a 
leader in financial products, recently released the latest measure of investor sentiment.7 
Practitioner interest in market sentiment measures reflects conventional market wisdom 
that investor sentiment affects stock values. Indeed, Tetlock (2007) confirms that 
financial media reporting affects investor sentiment and, ultimately, market values. 
                                                 
4 This study adopts the standard definition of mispricing as price deviations that contradict rational values implied by 
market efficiency. A necessary condition for determining mispricing is the assumption of a pricing model, such as the 
Capital Asset Pricing Model. 
5 Baker and Wurgler (2006) discuss episodes where sentiment anecdotally appears to drive market values. 
6 See, for example, the Barron’s weekly summary of various investor sentiment readings available at 
http://online.barrons.com/public/page/9_0210-investorsentimentreadings.html 
7 http://finance.yahoo.com/news/John-Hancock-Inaugurates-prnews-3389825386.html 
 16 
Thus, combined academic and practitioner interest in investor sentiment motivates a 
better understanding of the role it plays in asset pricing. 
This study examines the interaction between investor sentiment and industry returns. 
The study addresses three specific research questions: Does investor sentiment 
systematically predict industry returns? Does investor sentiment systematically affect 
the performance of industries that share certain characteristics? Lastly, does investor 
sentiment provide a practical signal for profitable industry rotation? These questions, 
from both financial theory and practical perspectives, remain largely unanswered. One 
complicating factor in studying the effect of sentiment on industry returns is the choice 
of an appropriate measure of investor sentiment. Baker and Wurgler (2006) comment, 
“There are no uncontroversial or universally accepted measures of investor sentiment.” 
Consequently, the analysis investigates industry return predictability using three 
different investor sentiment measures.8 Comparing different measures provides the 
study with added rigor and insight into the effect of investor sentiment on industry 
performance. 
The effect of investor sentiment on industry values is interesting from theoretical and 
practical perspectives. Fisher and Statman (2000), Brown and Cliff (2005) and Baker 
and Wurgler (2006) establish a positive correlation between investor sentiment and 
contemporaneous market mispricing, followed by predictable market reversals. Return 
predictability counters market efficiency theory. However, Barberis and Shleifer (2003) 
describe a behavioral style-investing theory. The theory posits that investors base their 
investments on styles (such as market capitalization or industry affiliation) rather than 
rational expectations. Empirical research, such as Baker and Wurgler (2006), already 
confirms the effect of sentiment on stocks that share common styles. Further, Kumar 
and Lee (2006), Edelen, Marcus, and Tehranian (2010) and Froot and Teo (2008) 
document investor herding in styles, which leads to mispricing. Small investors, 
moreover, are particularly prone to trade on sentiment. An analysis of the industry-level 
effect of sentiment helps better to understand its market-wide effect previously 
documented in the literature. Additionally, industries represent one of the important 
                                                 
8 This study fully discusses the three different investor sentiment measures in a subsequent section. 
 17 
style categories described by the Barberis and Shleifer (2003) model. The popularity of 
industry investing also makes an examination of industry return predictability 
interesting from a practical perspective. Yet, empirical research gives scant attention to 
the effect of investor sentiment on industry performance. 
The study’s main result is that investor sentiment has a market-wide effect rather 
than an industry-specific effect. Additionally, the results document only marginal 
strategy returns timing industry investments with investor sentiment. To begin, the 
results confirm previous research that investor sentiment predicts market mispricing, 
followed by reversals. Results similarly confirm a stronger effect of investor sentiment 
in equal-weighted indices. The results document the same pattern of investor sentiment 
predictability in industry returns. At a one-week horizon, investor sentiment positively 
predicts systematic performance, irrespective of the industry. Predictability turns largely 
negative over longer 8- to 52-week horizons. However, long-term predictability 
generally lacks statistical significance. Observing different periods of sentiment, 
industry reversal predictability is greater during bear markets than during bull markets. 
Contrasting prior firm-level studies, industry-wide characteristics, which act as a proxy 
for valuation uncertainty, do not systematically attract speculative mispricing. To that 
extent, markets appear more efficient at industry level. Lastly, the study evaluates the 
practical application of an industry rotation strategy that times investor sentiment. 
Results document statistically significant performance of 3 to 6 percent, which varies 
across time horizon, sentiment measure, and risk correction. While such a return may 
appear large enough for some, a sentiment rotation strategy would incur high turnover 
and transactions costs. 
Analysis of industry return predictability is subject to a battery of alternative 
robustness tests. One test examines whether the effect of sentiment on industry 
performance varies across sub-periods, dividing the full sample into equal 1987–1997 
and 1997–2007 sub-periods. The most notable difference is with BW sentiment, which 
overall has greater predictive power in the second period. Contrary to prior studies, 
American Association of Independent Investors (AAII) sentiment predictability does 
improve slightly in the later period. However, overall, the results remain largely 
comparable between the two sub-periods. Next, another test examines industry returns 
corrected for well-known sources of systematic risk. After a four-factor risk correction, 
 18 
short-term positive predictability decreases slightly, while negative long-term 
predictability noticeably increases. Thus, risk corrections do partially explain return 
predictability for some industries, while not for others. Lastly, a final test examines 
alternative industry classifications. The results are comparable for Fama and French 
(1993), Kacperczyk, Sialm, and Zheng (2005), and Global Industry Classification 
Standard (GICS) industry and sector classifications. Generally, the overall results on 
industry return predictability continue to hold regardless of the period, industry 
classification, or risk correction. 
This study contributes to the literature in two important respects. First, it documents 
limited cross-sectional differences in the effect of investor sentiment on predictable 
industry performance. Investor sentiment almost universally affects the performance of 
all industries at short horizons up to four weeks. Thus, the effect of investor sentiment 
broadly extends from the market to industries. However, unlike market studies, 
systematic long-term predictability is almost absent. An interpretation of the results here 
is that industry prices revert quickly to fundamental values. Additionally, industry 
values appear to correct sooner in bear markets. The implication is that arbitrage traders 
are able to correct mispricing sooner in bear markets than in bull markets, where short 
sales restrictions lead to delayed price corrections. Moreover, while the literature 
documents mispricing related to firm characteristics causing valuation difficulties, the 
results show no equivalent relationship with similar industry characteristics. 
Secondly, the study adds to a growing body of literature that investigates the 
practical importance of industry-level investment strategies. Moskowitz and Grinblatt 
(1999) provide evidence, for example, that industry return co-movement largely 
explains momentum trading profits. Moskowitz and Grinblatt (1999) argue that 
“investors simply herd toward (away from) hot (cold) industries, causing price pressures 
that create [return] persistence.” The results provide limited evidence that investors can 
profitably use investor sentiment to form a simple industry timing strategy. Effectively, 
the market rationally prices industry values, to the extent that strategy outperformance 
would quickly dissipate with reasonable transaction fees. The results thus do not support 
the Barberis and Shleifer (2003) model’s predictions of profitable style rotation trading 
strategies. Nonetheless, the study adds to others, such as Cavaglia, Brightman, and 
 19 
Aked (2000), Baca, Garbe, and Weiss (2000), and Phylaktis and Xia (2006), that 
explore the practical validity of industry-level investing. 
2.2 Literature Review and Hypotheses Development 
Industry prices periodically appear to reflect investor sentiment rather than 
fundamentals, in contradiction of market efficiency. Indeed, investor “irrational 
exuberance,” as described by Alan Greenspan, former chair of the U.S. Federal Reserve, 
can drive industry values, such as the 1990s technology bubble.9 Measures of investor 
sentiment, perhaps coincidentally, correspond with industry return co-movement, such 
as electronics in the 60s, gaming in the 70s, biotechnology in the 80s, Internet in the 
90s, and recently housing.10 Financial theory provides two possible explanations of 
industry return co-movement. First, market efficiency suggests that return co-movement 
reflects changes in common industry fundamentals. Second, behavioral finance suggests 
that irrational investor sentiment creates speculative price pressure, which can cause 
mispricing in industry prices. Behavioral finance further predicts that systematic 
patterns of industry momentum and reversals result when investors base their trades on 
sentiment, rather than on underlying fundamental values. 
Behavioral finance theory provides a mechanism that allows for price divergence 
from fundamental value. The Hong and Stein (1999) gradual information diffusion 
theory describes boundedly rational markets, populated by noise traders and momentum 
traders. Noise traders process price-relevant information slowly, which can lead to 
mispricing. Momentum traders construct simple trading models to exploit such 
mispricing, resulting in predictable short-term momentum and long-term price reversals. 
Similarly, the Barberis and Shleifer (2003) theory of style investing describes 
mispricing that results from investors’ inability to process firm-specific news. The style 
model posits that investors analyze investments grouped by styles, such as industries, 
rather than individual stock fundamentals. Additionally, the model holds that investors 
extrapolate future style performance from past performance and chase popular styles. 
This results in predictable return patterns and style mispricing. Peng and Xiong (2006) 
                                                 
9 http://www.federalreserve.gov/boarddocs/speeches/1996/19961205.htm 
10 Baker and Wurgler (2006) summarize well-known episodes of industry stock co-movement. 
 20 
extend the Barberis and Shleifer (2003) model. Specifically, Peng and Xiong (2006) 
articulate a style model in which traders focus on industry-level fundamentals rather 
than firm-level fundamentals. These theoretical models suggest the possibility that 
investor sentiment could predict industry returns, providing the basis for profitable 
rotation strategies. 
Empirical research provides evidence that investor sentiment results in market return 
predictability. For instance, Brown and Cliff (2005), Baker and Wurgler (2006), and 
Barber, Odean, and Zhu (2009) document a positive correlation between investor 
sentiment and market mispricing. Brown and Cliff (2005) evaluate investor sentiment 
predictability using Bakshi and Chen (2005) market mispricing and Investors 
Intelligence sentiment data from 1963 to 2000. The Bakshi and Chen (2005) study 
calculates market pricing errors as deviations from a discounted cash-flow model for the 
30 Dow Jones Industrial Average stocks. Brown and Cliff (2005) theorize that excessive 
investor sentiment results in market overvaluation, which reverts to fundamental value 
with a delay. The Brown and Cliff (2005) study observes statistically significantly 7 
percent price reversal in the first year following above-average investor sentiment. 
Baker and Wurgler (2006) provide evidence that stocks sharing certain characteristics 
that make objective valuations difficult are the most prone to speculative pricing. Stocks 
characterized by small market capitalization, high growth potential, or cash flow 
volatility are particularly susceptible to investor mispricing. Moreover, the stocks most 
susceptible to speculative pricing are also the most difficult and costly to arbitrage. 
Barber, Odean, and Zhu (2009) analyze the trade imbalances of individual investors, 
those most likely to trade on sentiment. They find that the correlated trades of individual 
investors cause prices to overshoot, which leads to predictable one-year reversals. 
Generally, empirical market research establishes that investor sentiment leads to short-
term mispricing followed by long-term price reversals. 
Research shows that individual and institutional investors herding in style categories, 
such as industries, leads to mispricing. The inclination to categorize stocks by style 
results from a human tendency to simplify complex analysis.11 Peng and Xiong (2006) 
                                                 
11 See, for instance, Feldman (2003). 
 21 
argue that individual investors, due to attention limitations, are prone to style herding. 
Moreover, Peng and Xiong (2006) show that the style herding of individual investors 
results in return co-movement, unrelated to fundamental values. 
Predictable cycles in asset returns require that investor mispricing has market impact. 
In a key study, Kumar and Lee (2006) investigate the effect of individual investor 
trading on stock market prices. Kumar and Lee (2006) analyse a buy-sell trade 
imbalance based on 1.85 million trades of 60,000 individual investors. The main 
conclusion of Kumar and Lee (2006) is that the collective trades of individual investors 
cause prices to deviate from fundamental value. Kumar and Lee (2006) observe that the 
trades of individual investors are highly correlated. Their study provides additional 
evidence that investors concurrently move in and out of similar groups of stocks. Kumar 
and Lee (2006) show that investor sentiment largely explains stock return co-movement, 
which is unrelated to systematic risk factors. Kumar and Lee (2006) also observe that 
individual investors prefer certain style categories (such as small-cap, value, and low-
priced stocks). Moreover, the styles preferred by individual investors are more subject 
to shifts in investor sentiment and predictable mispricing. Meanwhile, a study by Kumar 
(2009) shows that shifts in individual investor preferences also simultaneously affect the 
values of style opposites. For instance, when investors grow bullish about defensive 
industries, they concurrently grow bearish about cyclical industries. Empirical evidence 
also documents that individual investors concentrate on certain industries, such as 
technology, pharmaceuticals, retail, and telecom. 
Empirical research further shows that institutional investor style herding leads to 
market impact and return predictability. For instance, Froot and Teo (2008) find that 
institutional investor order flows positively predict short-term style performance, using 
data from 1995 to 2003. Froot and Teo (2008) observe that institutional investors rotate 
between style extremes, for instance cyclical and non-cyclical industries. Choi and Sias 
(2009) also document that the trades of institutional investors are highly correlated in 
certain industries, using trade data on institutional ownership from 1983 to 2005. Choi 
and Sias (2009) observe highly correlated trades into and out of the same industries, 
which collectively affect industry values. Moreover, Choi and Sias (2009) observe that 
institutional investors trade on past performance. Choi and Sias (2009) speculate that 
institutional investors herd together in popular industries to preserve their reputation. 
 22 
For instance, managers would chase popular industries, such as technologies in the 
1990s, to avoid the appearance of missing an investment opportunity. Equally, Jame and 
Tong (2009) also show that institutional investor order flows affect industry values. 
Jame and Tong (2009) find that institutional herding in popular industries accounts for 
60 percent of long-term portfolio underperformance. Lastly, Beber, Brandt, and 
Kavajecz (2010) document cyclical patterns in the correlated industry trades of 
institutional investors. Beber, Brandt, and Kavajecz (2010) argue that the cyclical 
trading patterns correspond with business cycle stages. However, trading patterns 
correlated with investor-sentiment cycles could provide an alternative explanation. 
Industries represent the best-understood and most widely followed style categories, 
for institutional and individual investors. Constituent industry firms share common 
fundamentals (such as product market, competition, regulations, and capital budgets). 
Industries, thus, present easily defined targets for investor herding. In contrast, other 
style categories, namely size and value, share less easily understood statistical 
commonality. Institutional investors are more likely to focus on fundamental rather than 
statistical categorization. Kacperczyk, Sialm, and Zheng (2005), for instance, show that 
mutual fund managers concentrate their investments in just a few industries. Popular 
media reporting on industries suggests the importance of industry styles to individual 
investors. The Wall Street Journal, Barron’s, and Business Week, for instance, provide 
regular commentary on the outlook for industry performance. Conversely, size and 
valuation styles receive scant mention in the financial press. The extensive availability 
of sector and industry financial products further suggests the popularity of those style 
categories with investors. 
A better understanding of industry performance is important from both academic and 
practical perspectives. Academic research indicates a link between industry 
performance and violations of market efficiency. Moskowitz and Grinblatt (1999), for 
instance, argue that industry momentum explains momentum profits. Indeed, Barberis 
and Shleifer (2003) predict that investor sentiment underlies momentum style returns, 
such as industry momentum. Hong, Torous, and Valkanov (2007) and Hou (2007) 
provide evidence of investor bias in processing industry news, which leads to cross-
industry return predictability. Other studies, such as Peng and Xiong (2006), show that 
informational inefficiency causes mispricing in small-cap industries. Research also 
 23 
supports the practical importance of industry-level investing. Cavaglia, Brightman, and 
Aked (2000), Baca, Garbe, and Weiss (2000), and more recently Phylaktis and Xia 
(2006) document that industry investing realizes greater diversification benefit than 
country investing. Moreover, the widespread availability of industry funds makes the 
implementation of industry investment strategies feasible from a practical perspective. 
Nonetheless, Rapach, Strauss, Tu, and Zhou (2010) comment on the lack of empirical 
research on industry investment strategies, a gap this study attempts to bridge. 
In sum, theoretical and empirical research supports a role for investor sentiment in 
asset price determination. Research shows that mispricing results from investors who 
base their trades on styles, such as industries, rather than underlying fundamentals. 
However, despite the academic and practical importance of better understanding 
industry performance, the literature has given the topic only limited attention.  
The above discussion leads to a formal statement of this study’s null and alternative 
hypotheses.  
H0: Industry return performance and cycles of investor sentiment are unrelated. 
H1: Investor sentiment systematically predicts industry returns. 
H2: Investors sentiment affects the returns of industries that share common 
characteristics. 
H3: Timing industry investments with investor sentiment facilitates profitable rotation 
strategies. 
In answering the research questions, this study contributes to the literature with a 
thorough examination of investor sentiment predictability of industry returns and the 
practicality of a simple strategy that times industry investments with investor sentiment. 
2.3 Investor Sentiment Measures and Market Return Data  
Of the many available investor sentiment measures, none is without its critics. To 
counter the criticism that the results are sample specific, the analysis investigates three 
different investor sentiment measures. Generally, the literature categorizes investor 
sentiment measures as direct or indirect measures, (Brown and Cliff (2004)). Investor 
surveys provide direct measures of investor sentiment. Historical financial data provides 
 24 
indirect measures of investor sentiment. The analysis uses two direct measures and one 
indirect measure of investor sentiment.12 
2.3.1 Sentiment measures 
The American Association of Independent Investors (AAII) survey is one of the direct 
investor sentiment measures. The AAII survey measures the sentiment of small 
investors. The literature commonly describes small investors as noise traders, who are 
prone to trade on sentiment rather than fundamental analysis.13 Studies by Kumar and 
Lee (2006) and Schmeling (2009), among others, also provide empirical evidence that 
small investors herd in particular stock categories, such as small-cap stocks. Moreover, 
such market studies show that the collective trades of small traders cause predictable 
mispricing. The AAII conducts a weekly survey of its members on their view of future 
market direction. Specifically, the survey asks members whether they have a bullish, 
neutral, or bearish stock market outlook for the next six months.14 Prior to January 
2000, the AAII mailed its survey to a random selection of 200 members. Since then, the 
AAII has conducted an online survey, which is available to all registered members. The 
AAII publishes its survey results every Thursday. Historical data is available online 
from 24/07/1987 at no cost. The AAII data comes from the association’s website.15 
The Investors Intelligence (II) survey is the other direct investor sentiment measure. 
Brown and Cliff (2004) argue that the II survey proxies the sentiment of professional 
investors, as newsletter writers are mostly retired institutional traders. The financial 
media, such as The Wall Street Journal, widely report II survey results. The II survey 
reflects the sentiment of financial newsletter writers. Editors at Investor Intelligence 
categorize the sentiment of newsletter writers from a selection of approximately 150 
newsletters as bullish, bearish or correction. The categorization of newsletter sentiment 
is a subjective process. However, Investors Intelligence editorial staffing has been 
consistent, with the same two editors since the survey’s inception in 1963.16 The 
newsletters included in the survey do change over time, with continual additions and 
                                                 
12 Qiu and Welch (2005) provide a good discussion of differences in investor sentiment measures. 
13 See, for example, Black (1986) and Barber, Odean, and Zhu (2009). 
14 http://www.aaii.com/sentimentsurvey 
15 http://www.aaii.com/files/surveys/sentiment.xls 
16 http://www.investorsintelligence.com/x/us_advisors_sentiment.html 
 25 
deletions. Newsletters enter the survey only after they have been in print for a period of 
months. Fisher and Statman (2000) conclude that the II survey provides a measure of 
investor sentiment distinct from the AAII survey. Investors Intelligence releases its 
survey results each Thursday. Historical data is available by subscription from 
01/04/1963. The II survey data comes directly from Investor Intelligence. 
The analysis uses a bull-bear spread for both the AAII and II surveys, calculated as 
the difference between the reported measure of bullish and bearish sentiment. The AAII 
and II surveys report bullish sentiment, bearish sentiment and neutral/correction 
sentiment as a percentage of the total survey results. An example illustrates the bull-bear 
calculation. For instance, on 24 July 1987, the AAII survey results show bull, bear, and 
neutral investor sentiment at 36, 14, and 50 percent. The bull-bear spread for that period 
is therefore 22 percent, calculated as 36 minus 14 percent. Alternatively, one could 
calculate a bull/bear ratio. However, the financial media widely report on bull-bear 
spreads. For instance, The Wall Street Journal and Barron’s report weekly bull-bear 
spreads for both the AAII and II surveys. Other studies, such as Fisher and Statman 
(2000) and Brown and Cliff (2005), similarly use the bull-bear spread, also citing its 
popularity with practitioners. As such, the analysis adopts the bull-bear spread as the 
preferable measure. Results are, however, robust to the use of either bull-bear spreads or 
bull/bear ratios. 
The Baker and Wurgler (2006) index is the indirect investor sentiment measure. 
Baker and Wurgler (2006) argue that no investor sentiment measure is universally 
accepted. As such, Baker and Wurgler (2006) construct their sentiment index as the first 
principal component of six common investor sentiment proxies described in the 
literature. The six proxies are closed end fund discounts; NYSE share turnover; the 
number of initial public offerings (IPO); first day average IPO returns; the percentage of 
equity in capital budgets; and the return premium between dividend-paying and 
dividend-non-paying firms. Baker and Wurgler (2006) interpret the first principal 
component of these six variables as the common link to investor sentiment. The Baker 
and Wurgler (2006) index is available at a monthly frequency from July 1965 to 
December 2007. To match AAII and II survey frequencies, the study constructs a 
weekly index (BW) from the Baker and Wurgler (2006) monthly index. Each week 
during a month assumes the month-end value of the Baker and Wurgler (2006) index. 
 26 
As such, the analysis assumes that the month-end sentiment prevails throughout the 
month. The Baker and Wurgler (2006) sentiment index comes from Jeffrey Wurgler’s 
website.17 
Baker and Wurgler (2006) construct a level sentiment index and a change index. The 
change index consists of the first principal component of changes in each individual 
proxy. The level-index series contains an explosive unit root, which consequently 
invalidates the economic and statistical inferences of predictive regressions.18 The 
analysis uses the Baker and Wurgler (2006) change index, which has no unit root and 
thus allows reliable estimations. 
Figure 2.1 Investor sentiment cycles 
 
Notes: illustrates investor sentiment cycles for the American Association of Independent Investors (AAII), Investors 
Intelligence (II), and Baker & Wurgler sentiment measures. The shaded areas indicate NBER defined periods of 
economic recession. The figure also indicates peaks in industry valuation cycles. 
Figure 2.1 provides a graphical comparison of the AAII, II, and BW investor 
sentiment measures. To facilitate comparison, the figure uses a six-month moving 
                                                 
17 http://pages.stern.nyu.edu/~jwurgler/ 
18 See, for example, Campbell and Yogo (2006). 
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Investor Sentiment Measures
NBER Recessions Baker & Wurgler Index American Association of Independent Investors Investors Intelligence
Technology
Industry
Banking 
Industry
Biotech
Industry
Housing 
Industry
 27 
average of normalized sentiment measures. Shaded areas denote National Bureau of 
Economic Research (NBER) periods of economic recession. All three measures indicate 
periodic sentiment spikes. The BW sentiment index particularly spikes during the 
technology industry boom, reflecting the inclusion of IPO returns and IPO issuance as 
components of that index. While different, the AAII and II sentiment measures do 
visually move closely together, particularly subsequent to the 2001 recession. The 
sentiment of small investors, measured by AAII, if anything, lags behind the sentiment 
of newsletter writers, measured by II. A logical interpretation is that small investors 
react with a delay to financial newsletter writers. 
Table 2.1 Descriptive statistics for investor sentiment and market indices 
 
Notes: Table 2.1 reports descriptive statistics for investor sentiment measures and market index returns. Column 4 
reports observations for the total sample from 24/07/1987 to 28/12/2007. Columns 2 and 3 report observations for 
periods of bullish and bearish investor sentiment. A positive (negative) bull-bear spread defines bull (bear) markets, 
for each investor sentiment measure. The investor sentiment measures come from the American Association of 
Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). The small-stock index is an 
equally weighted index of the bottom capitalization decile of all stock in the CRSP database. The growth-stock index 
is an equally weighted index of the bottom book-to-market (BE/ME) ratio decile of all stocks in the CRSP database. 
The table reports annualized means, medians, and standard deviations. Table also reports first-order autocorrelations 
(t-1) and cross-correlations (i,j) for each sentiment measure. 
Table 2.1 confirms that the AAII and II measures have a relatively high .52 
correlation coefficient. In contrast, the BW indirect sentiment measure has a low 
correlation of approximately .17 with both survey measures. The Investors Intelligence 
bull-bear spread has the highest mean (.12) and autocorrelation (0.95). Clarke and 
Statman (1998) argue that the sentiment of newsletter writers reflects their expectation 
that short-term market trends will continue, as the high autocorrelation would indicate. 
The volatility of AAII is slightly higher than II at 18 and 16 percent. Indro (2010) 
suggests that the higher AAII volatility results from greater sentiment diversity between 
Bull Bear Total Mean Median Stdev  t-1  i,AAII  i,II  i,BW
Sentiment Measures:
AAII sentiment 739 328 1067 0.10 0.11 0.18 0.663 1.000 0.517 0.170
II sentiment 866 201 1067 0.12 0.14 0.16 0.955 0.517 1.000 0.172
BW sentiment 560 507 1067 -0.01 0.05 1.02 0.799 0.170 0.172 1.000
Market Indices:
CRSP value-weighted index 1067 0.10 0.19 0.15 -0.035 0.046 0.032 0.045
CRSP equal-weighted index 1067 0.23 0.31 0.14 0.252 0.194 0.139 0.096
S&P  500 value-weighted index 1067 0.09 0.15 0.15 -0.077 0.027 0.017 0.041
S&P  500 equal-weighted index 1067 0.10 0.18 0.15 -0.010 0.081 0.055 0.039
Small stock equal-weighted index 1067 0.11 0.18 0.16 0.130 0.213 0.167 0.104
Growth stock equal-weighted index 1067 0.07 0.17 0.21 0.076 0.065 0.044 0.050
Sample Period  from 24/07/1987 to 28/12/2007
Observations
 28 
small investors and newsletter writers. In overall comparison, each measure appears to 
provide sufficiently different aspects of investor sentiment. 
2.3.2 Market data 
Market index data comes from multiple sources. All Centre for Research in Security 
Prices (CRSP) stock market data comes from the Kenneth French website.19 The CRSP 
market data comprises all NYSE, AMEX, and NASDAQ listed stocks. The Standard & 
Poor’s 500 index data comes from Global Financial Data and Data Stream.20 The small 
stock index represents the bottom NYSE breakpoint capitalization decile of all stocks in 
the CRSP database. The growth stock index represents the bottom NYSE breakpoint 
book-to-market ratio (BE/ME) decile of all stocks in the CRSP database. Prior research, 
by Baker and Wurgler (2006) and Kumar and Lee (2006), concludes that investor 
sentiment particularly affects the valuation of small-cap stocks. As such, the analysis 
examines both value- and equal-weighted indices. The one-month Treasury bill, from 
Ibbotson Associates and downloaded from the Kenneth French website, serves as a 
proxy for the risk free rate. The common period of data availability for the AAII, II, and 
BW sentiment measures determines the sample period from 24/07/1987 to 28/12/2007. 
Table 2.1 reports market index descriptive statistics and correlations with each investor 
sentiment measures, with annualized means, medians, and standard deviations. 
Table 2.1 provides at least two important preliminary observations. First, all 
sentiment measures have higher correlations with the equal-weighted indices than the 
value-weighted indices. The S&P 500 value index has the lowest investor sentiment 
correlation, along with return and standard deviation. The S&P 500 index represents the 
500 largest capitalized U.S. stocks; as such, low sentiment correlations are no surprise. 
Additionally, the literature documents a greater effect of investor sentiment in small-cap 
stocks. Individual investors, described in the literature as inclined to invest on 
sentiment, also prefer to invest in small-cap stocks. Therefore, the expectation is to 
observe larger correlations between small-cap stocks and individual investor sentiment, 
as is the case. The largest correlations are between AAII sentiment and the equal-
                                                 
19 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
20 https://www.globalfinancialdata.com/platform/Welcome.aspx 
 29 
weighted small-cap index (0.21) and the CRSP equal-weighted index (0.19). The CRSP 
equal-weighted index has, on average, the highest correlation across all three measures 
and the highest return. The growth stock equal-weighted index has the lowest sentiment 
correlation among the equal-weighted indices. 
Secondly, the AAII investor sentiment measure has the highest market correlations, 
while the Baker and Wurgler (2006) index has the smallest correlations. Again, this 
confirms prior literature that shows sentiment has the most prevalent effect on the 
investment decisions of individual investors. Another important distinction is that the 
AAII and II direct survey measures reflect contemporaneous investor sentiment. In 
contrast, the Baker and Wurgler (2006) index is, by construction, backward looking, 
originating from historical data. Generally, Table 2.1 shows greater correlations 
between direct investor sentiment measures and equal-weighted indices. 
2.4 Markets and Sentiment 
This section verifies that investor sentiment predicts market returns, as prior empirical 
research documents. Fisher and Statman (2000), for instance, investigate the ability of 
AAII and II investor sentiment surveys to predict S&P 500 returns. Fisher and Statman 
(2000) find that the AAII investor sentiment surveys negatively forecast market returns 
from 1985 to 1998. Similarly, Brown and Cliff (2005) document that the II bull-bear 
spread predicts long-term market-pricing errors. Moreover, their study shows that II 
predictability is distinct from previously described predictor variables. Siegel (2002) 
also describes a profitable trading strategy that uses the Investors Intelligence survey to 
forecast market direction. Indro (2010) investigates the relationship between investor 
trade flows and the AAII and II sentiment surveys. The Indro (2010) study documents a 
significant relationship between investor sentiment and fund flows, which in turn leads 
to market mispricing. 
International studies provide further evidence that investor sentiment predicts market 
returns. Wurgler, Baker, and Yuan (2009), for example, show that national investor 
sentiment measures negatively predict market index returns in six countries. Similarly, 
Schmeling (2009) shows that national measures of investor sentiment negatively predict 
stock market returns in 18 countries. Schmeling (2009) observes a greater effect of 
investor sentiment in culturally “tight-knit” countries, which he attributes to greater 
 30 
investor herding tendencies. Taken together, the empirical evidence provides 
convincing evidence that investor sentiment predicts market returns. 
Table 2.2 Investor sentiment predictability of index returns 
 
Notes: Table 2.2 reports the a1 regression coefficients from Equation 2.1. Equation 2.1 estimates investor sentiment predictability of excess market returns at the indicated k-week lags over the sample period 24/07/1987 to 28/12/2007. 
The analysis uses the American Association of Independent Investors (AAII), Investors Intelligence (II), and Baker 
and Wurgler (BW) sentiment measures. The small-stock index is an equally weighted index of the bottom 
capitalization decile of all stocks in the CRSP database. The growth-stock index is an equally weighted index of the 
bottom book-to-market ratio (BE/ME) decile of all stocks in the CRSP database. Bold indicates 10 percent or greater 
statistical significance estimated with White (1980) standard errors. 
, 0 1 , ,i t s t k i tR a a Sent e                 (Eq. 2.1) 
Table 2.2 reports the a1 coefficients from Equation 2.1. The equation runs a 
regression of excess market returns (Ri) on a constant and each investor sentiment 
measure (Senti) for different k-week lags. The a1 coefficients measure investor 
sentiment predictability of excess market returns. Based on prior studies, the 
expectation is to observe positive short-term a1 coefficients and negative long-term a1 
coefficients. Bold indicates 10 percent or greater statistical significance estimated with 
White (1980) standard errors. 
Sentiment Market Indices Lag 01 Lag 08 Lag 13 Lag 26 Lag 52
AAII CRSP value-weighted index 0.009 0.001 0.001 0.000 -0.002
CRSP equal-weighted index 0.024 0.002 -0.001 -0.001 -0.004
S&P  500 value-weighted index 0.006 0.000 0.002 0.000 -0.001
S&P  500 equal-weighted index 0.012 0.001 0.001 -0.002 0.000
Small stock equal-weighted index 0.027 0.006 -0.002 -0.002 -0.004
Growth stock equal-weighted index 0.013 -0.002 -0.006 0.000 -0.008
II CRSP value-weighted index 0.004 -0.010 -0.008 -0.002 -0.006
CRSP equal-weighted index 0.018 -0.009 -0.009 -0.002 -0.007
S&P  500 value-weighted index 0.002 -0.009 -0.007 -0.003 -0.006
S&P  500 equal-weighted index 0.008 -0.006 -0.005 -0.002 -0.002
Small stock equal-weighted index 0.024 -0.006 -0.008 0.002 -0.005
Growth stock equal-weighted index 0.008 -0.013 -0.011 0.000 -0.011
BW CRSP value-weighted index 0.001 -0.001 0.000 0.001 -0.001
CRSP equal-weighted index 0.002 -0.002 0.000 0.001 -0.002
S&P  500 value-weighted index 0.001 -0.001 0.000 0.001 -0.001
S&P  500 equal-weighted index 0.001 -0.001 0.000 0.000 -0.001
Small stock equal-weighted index 0.001 -0.001 0.000 0.001 -0.002
Growth stock equal-weighted index 0.001 -0.001 0.001 0.001 -0.003
Sample period 24/07/1987 - 28/12/2007
 31 
The results reported in Table 2.2 confirm investor sentiment predictability of market 
returns. Studies, such as Brown and Cliff (2005), similarly document positive short-
term and negative long-term market return predictability.21 The interpretation is that 
investor enthusiasm causes prices initially to overshoot, before a delayed price reversion 
to fundamental value. All statistically significant a1 coefficients, at a one-week lag, have 
the expected positive sign. The a1 coefficients are more significant and larger for the 
equal-weighted indices. The results confirm Baker and Wurgler (2006), among others, 
who document that investor sentiment has a more pronounced effect on small-cap 
stocks. The statistically significant a1 coefficients at 8- to 52-week lags, with one 
exception, also have the expected negative sign. Here again, at long-term horizons, 
investor sentiment has greater predictability of equal-weighted returns. Interestingly, 
there is no statistically significant predictability at a lag of 26 weeks. Predictability is 
greatest at a lag of 52 weeks, mostly for the Baker and Wurgler (2006) index. Generally, 
however, the Investors Intelligence survey provides the greatest long-term return 
predictability and the AAII survey the least. 
2.5 Industries and Sentiment 
The analysis in this section documents investor sentiment predictability of industry 
returns. The previous findings verify that investor sentiment reliably predicts the 
market, particularly for equal-weighted indices. An analysis of industry returns, in part, 
may help to understand better the drivers of investor sentiment predictability observed 
at market level. For instance, certain industries, especially exposed to investor 
sentiment, potentially underlie market sentiment. The Barberis and Shleifer (2003) and 
Peng and Xiong (2006) models envisage that investor herding in particular styles, such 
as industries, leads to predictable mispricing and reversals. Baker and Wurgler (2006) 
also provide anecdotal evidence that industry valuation cycles drive market cycles. For 
instance, the dramatic increase in Internet stock values elevated the entire stock market 
during the late 1990s. Moskowitz and Grinblatt (1999) also provide empirical evidence 
that hot and cold industries underlie market momentum. The following analysis 
examines industry returns for statistically significant investor sentiment predictability. 
                                                 
21 See for instance Baker and Wurgler (2006), Brown and Cliff (2005), and Lemmon and Portniaguina (2006). 
 32 
Additional analysis then evaluates the economic significance of industry return 
predictability. Lastly, this section then investigates whether investor sentiment affects 
industry returns differently during periods of bullish and bearish sentiment. 
2.5.1 Description of industry data  
The main analysis investigates the effect of investor sentiment on industry returns using 
the Fama and French 49 industry portfolios.22 The Fama and French industry 
classification maps all NYSE, AMEX, and NASDAQ stocks to one of 49 industry 
portfolios, using the Standard Industrial Classification (SIC).23 Prior investor sentiment 
studies, notably Kumar (2009) and Choi and Sias (2009), similarly use the Fama and 
French 49 industry classification. The analysis focuses on the Fama and French equal-
weighted industry returns. Baker and Wurgler (2006) argue that, “large firms will be 
less affected by sentiment, and hence value weighting will obscure the more relevant 
patterns.” The earlier analysis also confirms investor sentiment has a greater effect on 
equal-weighted indices. Unfortunately, the Fama and French industry portfolios are only 
available at daily and monthly frequencies. Analysis using daily data is inappropriate as 
it is noisy and, most importantly, because daily data does not match the weekly 
frequency of AAII and II investor sentiment data. Fortunately, a resolution to those 
issues simply requires constructing weekly returns by compounding daily returns over 
each weekly period. The resultant weekly industry return series have 1224 observations 
that start on 24/07/1987 and end on 28/12/2007. 
Table 2.3 provides descriptive statistics for the Fama and French 49 industries. The 
table reports annualized industry returns and standard deviations. The second column 
reports the average number of industry constituent firms. The number of industry firms 
is important for two reasons. First, idiosyncratic risk may dominate the returns of 
industry portfolios with a small number of firms. For instance, the tobacco, soft drink, 
and coal industries contain fewer than 10 firms each. As such, the observed effect of 
sentiment on those industries may reflect fundamental news affecting firm values. 
Additionally, the number of firms indicates the level of industry competition. Hoberg & 
                                                 
22 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
23 See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html for more information. 
 33 
Phillips (2010) theorize that firms in highly competitive industries are more prone to 
cash flow uncertainty than non-competitive industries. The table also reports a single-
index market beta to measure industry exposure to market risk. Size is the average 
market capitalization in millions of U.S. dollars for industry constituent firms. The table 
also reports average industry book-to-market equity (BE/ME) valuations as a growth 
proxy. Small stocks and growth stocks are especially subject to investor sentiment 
(Baker and Wurgler (2006)). The final three columns report industry return correlations 
with the AAII, II, and BW investor sentiment measures. The bottom row reports 
average statistics across all industries. 
Investor sentiment correlations with industry returns vary widely. Similar to the 
market, average industry correlations with the AAII sentiment measure (0.14) are the 
highest and BW sentiment measure (0.07) the lowest. Generally, it appears that returns 
for competitive industries – those with a large number of firms – have higher investor 
sentiment correlations than non-competitive industries. For instance, the business 
services industry, with 280 firms, has the highest AAII sentiment correlation (0.19). In 
contrast, the tobacco industry, with five firms, has the lowest AAII sentiment 
correlation (0.06). Additionally, industries characterized by many small-cap firms, such 
as business services (0.19), wholesale (0.19), and lab equipment (0.18), have the highest 
AAII sentiment correlations. Conversely, industries characterized by a few large-cap 
firms have the lowest sentiment correlations. For instance, the tobacco industry has only 
5 highly capitalized firms and the smallest AAII sentiment correlation (0.03). No 
obvious pattern appears between sentiment correlations and industry standard 
deviations, betas, and valuation ratios. 
  
 34 
Table 2.3 Industry descriptive statistics 
 
Notes: Table 2.3 reports industry descriptive statistics for the weekly Fama and French 49 equal-weight industry 
portfolio returns from 24/07/1987 to 28/12/2007. The descriptive statistics include the average number of industry 
firms, annualized means, annualized standard deviations, single-index market betas, average industry firm market 
capitalization (millions USD), and average industry book-to-market valuation ratios. The last three columns report 
cross-correlations (i,s) between industry returns and the American Association of Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW) investor sentiment measures. The bottom row reports 
averages for all industries. 
Industry Firms Mean Stdev BetaMkt Size B/M  i,AAII  i,II  i,BW
Agric 16 0.221 0.246 0.76 1,255 0.37 0.12 0.11 0.04
Food 85 0.197 0.137 0.59 2,508 0.37 0.13 0.12 0.06
Soda 8 0.159 0.240 0.65 1,914 0.65 0.10 0.05 0.05
Beer 15 0.190 0.166 0.51 12,677 0.19 0.11 0.09 0.04
Smoke 5 0.203 0.297 0.63 17,376 0.40 0.06 0.03 -0.03
Toys 45 0.212 0.222 0.83 448 0.39 0.17 0.12 0.11
Fun 81 0.229 0.229 0.97 1,423 0.54 0.18 0.13 0.10
Books 49 0.143 0.211 0.89 1,641 0.44 0.15 0.10 0.11
Hshld 89 0.190 0.195 0.86 2,947 0.30 0.16 0.16 0.09
Clths 73 0.204 0.215 0.90 709 0.41 0.15 0.17 0.11
Hlth 103 0.278 0.203 0.82 678 0.42 0.16 0.10 0.07
MedEq 173 0.292 0.201 0.84 860 0.27 0.18 0.11 0.10
Drugs 261 0.268 0.264 1.07 2,675 0.21 0.16 0.10 0.08
Chems 86 0.195 0.208 0.95 2,253 0.45 0.15 0.10 0.06
Rubbr 49 0.234 0.208 0.78 468 0.45 0.18 0.11 0.08
Txtls 30 0.113 0.237 0.86 584 0.74 0.13 0.11 0.09
BldMt 92 0.248 0.222 0.80 937 0.49 0.15 0.08 0.07
Cnstr 63 0.210 0.274 1.10 789 0.57 0.14 0.17 0.07
Steel 68 0.168 0.267 1.17 1,146 0.72 0.15 0.13 0.08
FabPr 22 0.186 0.257 0.96 195 0.64 0.13 0.07 0.07
Mach 174 0.230 0.218 1.04 1,245 0.45 0.16 0.12 0.08
ElcEq 79 0.282 0.213 0.94 1,637 0.36 0.19 0.07 0.13
Autos 66 0.160 0.254 1.10 1,745 1.02 0.16 0.13 0.10
Aero 23 0.235 0.221 0.87 5,328 0.48 0.17 0.15 0.09
Ships 10 0.126 0.274 0.91 1,573 0.53 0.14 0.13 0.04
Guns 10 0.213 0.240 0.64 2,587 0.50 0.14 0.09 0.00
Gold 22 0.320 0.428 0.45 1,291 0.35 0.04 0.07 -0.03
Mines 18 0.261 0.280 0.93 1,397 0.51 0.11 0.09 0.05
Coal 7 0.133 0.405 1.22 1,650 0.78 0.09 0.08 -0.06
Oil 182 0.280 0.266 0.96 3,686 0.55 0.15 0.08 -0.01
Util 153 0.124 0.135 0.53 2,756 0.94 0.10 0.06 0.00
Telcm 129 0.188 0.253 1.12 4,473 0.62 0.13 0.07 0.13
PerSv 58 0.237 0.206 0.86 633 0.43 0.16 0.12 0.10
BusSv 280 0.264 0.192 0.91 707 0.33 0.19 0.12 0.13
Hardw 158 0.258 0.275 1.18 3,201 0.36 0.14 0.06 0.13
Softw 337 0.290 0.261 1.12 1,575 0.20 0.15 0.07 0.14
Chips 292 0.283 0.274 1.21 1,525 0.37 0.15 0.08 0.12
LabEq 116 0.343 0.226 1.02 603 0.40 0.18 0.07 0.12
Paper 65 0.142 0.214 0.95 2,256 0.51 0.12 0.10 0.05
Boxes 14 0.151 0.234 0.93 1,402 0.49 0.11 0.05 0.06
Trans 110 0.193 0.209 0.95 1,477 0.75 0.15 0.12 0.08
Whlsl 203 0.246 0.187 0.87 668 0.47 0.19 0.15 0.12
Rtail 264 0.201 0.216 1.01 2,405 0.34 0.13 0.13 0.08
Meals 99 0.239 0.192 0.81 1,059 0.35 0.16 0.16 0.09
Banks 552 0.215 0.150 0.62 1,516 0.59 0.14 0.10 0.04
Insur 190 0.199 0.176 0.81 2,872 0.67 0.12 0.11 0.04
RlEst 36 0.218 0.223 0.77 399 0.62 0.15 0.14 0.10
Fin 320 0.256 0.176 0.82 1,677 0.54 0.14 0.11 0.10
Other 73 0.235 0.191 0.76 4,372 0.37 0.20 0.13 0.11
Average 0.218 0.230 0.88 2,269 0.49 0.14 0.10 0.07
 35 
2.5.2 Basic industry regressions 
The analysis now examines whether investor sentiment systematically predicts industry 
returns. Additionally, the analysis further investigates cross-sectional differences in the 
effect of investor sentiment on industry performance. Equation 2.2 runs a regression of 
excess industry returns (Ri) on a constant, the sentiment measures (Sents) for the 
indicated k-week lag, and the market risk premium (Rm). The variable of interest in 
Equation 2.2 is the a1 regression coefficient. The interpretation of the a1 coefficient is 
industry outperformance predicted by investor sentiment. Effectively, the a0 and a1 
coefficients together represent a traditional Jensen’s alpha. Bold indicates statistical 
significance of 10 percent or greater estimated with White (1980) standard errors. The 
bottom two rows of Table 2.4 report the number of significantly positive or negative a1 
regression coefficients. 
, 0 1 , 0 ,i t s t k m t tR a a Sent b R e                  (Eq. 2.2) 
Table 2.4 reports the a1 regression coefficient from Equation 2.2. At a casual glance, 
Table 2.4 reveals investor sentiment systematically predicts returns for a high 
percentage of industries. Nonetheless, the economic magnitude of investor sentiment, as 
measured by the size of the a1 coefficients, varies considerably. Based on market 
studies, the expectation is that initial investor overreaction causes short-term positive 
predictability. Significant and negative return predictability at longer horizons would 
indicate industry price reversion to fundamental value. The a1 coefficients on all 
sentiment measures have the correct positive sign at a one-week lag, with one 
exception. The a1 coefficients on AAII sentiment are, on average, the largest of all 
sentiment measures. The AAII sentiment measures are all statistically significant, with 
the exception of precious metals (gold). In contrast, the BW index has significant 
coefficients at a one-week lag for only about 60 percent of all industries. 
Investor sentiment predictability provides mixed results at longer horizons. At an 
eight-week lag, there is a ratio of 2:3 positive to negative a1 coefficients. Predictability 
drops substantially at 13- to 52-week horizons, along with the magnitude of a1 
coefficients. Most statistically significant a1 coefficients have the expected negative 
sign at 13-week and 52-week horizons. As with the market, investor sentiment has the 
 36 
least significant predictability at a 26-week lag. Generally, the BW index provides the 
least predictability and the AAII measure the greatest, at all horizons. 
Table 2.4 Investor sentiment predictability of industry returns 
 
Notes: Table 2.4 reports the a1 coefficients estimated with Equation 2.2. The equation runs a regression of excess industry returns on a constant, sentiment measures for the indicated k-week lags, and the market-risk premium. 
Sentiment measures are from the American Association of Independent Investors (AAII), Investors Intelligence (II), 
and Baker and Wurgler (BW). Bold indicates statistical significance of 10 percent or greater estimated with White 
(1980) standard errors. 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.019 0.019 0.000 0.012 0.007 0.000 0.000 0.002 -0.001 0.008 0.004 0.001 0.003 0.000 0.001
Food 0.010 0.011 0.001 0.002 -0.002 -0.001 0.000 -0.003 0.000 0.001 0.002 0.000 -0.003 -0.002 0.000
Soda 0.013 0.009 0.001 0.009 0.001 -0.002 0.006 0.003 0.000 -0.001 0.002 0.000 0.001 0.000 0.001
Beer 0.011 0.011 0.000 0.001 -0.001 0.000 0.002 0.004 -0.001 0.003 0.008 0.000 0.001 0.002 -0.001
Smoke 0.011 0.005 -0.002 -0.006 -0.012 -0.001 0.000 -0.008 0.000 0.004 -0.004 -0.002 0.004 0.000 0.000
Toys 0.021 0.019 0.002 0.003 -0.005 -0.001 -0.003 -0.004 0.000 0.000 -0.002 0.000 -0.008 -0.008 -0.002
Fun 0.022 0.019 0.002 0.003 -0.002 -0.001 0.002 -0.004 -0.001 0.002 0.000 0.000 0.001 0.002 -0.001
Books 0.015 0.012 0.002 0.001 -0.005 -0.001 -0.002 -0.005 0.000 0.004 0.003 0.000 -0.002 0.000 -0.001
Hshld 0.019 0.019 0.001 0.003 -0.001 -0.001 -0.003 -0.002 -0.001 -0.004 -0.001 0.000 -0.002 -0.001 0.000
Clths 0.020 0.024 0.002 0.000 0.001 -0.002 -0.003 -0.004 -0.001 -0.002 0.000 0.000 0.001 0.000 -0.001
Hlth 0.016 0.015 0.001 0.002 -0.009 -0.001 -0.003 -0.010 -0.001 -0.001 -0.001 0.000 -0.007 -0.008 0.000
MedEq 0.020 0.015 0.002 0.002 -0.006 0.000 -0.006 -0.009 -0.001 -0.001 -0.002 0.000 -0.009 -0.010 -0.001
Drugs 0.024 0.018 0.002 0.002 -0.003 0.000 -0.010 -0.003 -0.001 -0.001 0.003 0.001 -0.015 -0.016 -0.002
Chems 0.013 0.012 0.001 0.002 0.000 -0.001 0.003 0.002 0.000 -0.002 0.002 0.000 -0.003 0.003 0.000
Rubbr 0.020 0.016 0.001 0.000 -0.003 -0.001 -0.002 -0.003 0.000 -0.003 -0.001 0.000 -0.003 0.000 -0.001
Txtls 0.018 0.016 0.002 0.001 -0.004 -0.001 0.001 -0.007 -0.001 -0.007 -0.005 0.000 -0.005 0.000 -0.001
BldMt 0.018 0.013 0.001 0.004 0.002 -0.001 0.003 0.000 0.000 -0.002 0.000 0.000 -0.001 -0.001 0.000
Cnstr 0.024 0.027 0.001 0.002 -0.001 0.000 -0.003 -0.001 -0.001 -0.002 -0.002 0.000 -0.006 -0.005 -0.001
Steel 0.019 0.020 0.001 0.007 0.005 -0.001 0.000 0.005 -0.001 -0.003 0.008 0.002 -0.003 0.008 0.000
FabPr 0.017 0.010 0.001 0.002 -0.004 -0.001 -0.005 -0.007 0.000 0.000 0.005 0.001 -0.006 0.001 0.000
Mach 0.016 0.016 0.001 0.004 -0.001 0.000 -0.004 -0.005 0.000 -0.002 0.002 0.000 -0.004 0.001 0.000
ElcEq 0.021 0.010 0.003 0.002 -0.007 0.001 -0.002 -0.007 0.001 -0.003 -0.006 0.001 -0.006 -0.005 0.000
Autos 0.020 0.018 0.002 0.001 -0.004 -0.001 0.000 -0.003 -0.001 -0.001 0.001 0.000 -0.004 -0.001 0.000
Aero 0.021 0.023 0.002 0.006 0.005 0.000 0.012 0.005 0.000 0.003 0.004 0.001 -0.004 0.003 0.000
Ships 0.025 0.025 0.001 0.007 0.005 -0.001 0.000 0.008 -0.001 -0.008 0.003 -0.001 -0.005 -0.002 0.000
Guns 0.021 0.018 0.000 0.007 0.006 -0.001 0.006 0.007 -0.001 -0.001 0.003 -0.001 -0.003 0.001 0.000
Gold 0.010 0.025 -0.002 0.015 0.020 0.001 0.006 0.008 -0.002 -0.014 0.000 0.004 -0.003 -0.007 0.004
Mines 0.015 0.018 0.001 0.010 0.012 -0.001 0.001 0.009 0.000 -0.004 -0.001 0.001 -0.009 0.003 0.001
Coal 0.015 0.021 -0.003 0.015 0.014 0.000 0.015 0.021 -0.001 0.011 0.010 0.003 -0.001 0.005 0.002
Oil 0.015 0.013 -0.001 0.016 0.011 0.001 0.009 0.010 0.001 -0.001 -0.001 0.002 -0.002 0.001 0.000
Util 0.005 0.005 0.000 0.002 0.003 0.000 0.003 0.005 0.000 0.002 0.002 -0.001 0.000 0.004 0.000
Whlsl 0.015 0.012 0.003 -0.007 -0.008 -0.001 -0.009 -0.012 0.001 0.000 0.000 0.002 -0.004 0.000 0.000
PerSv 0.019 0.017 0.002 -0.003 -0.003 -0.001 -0.008 -0.006 -0.001 -0.001 0.003 0.000 -0.006 -0.004 0.000
BusSv 0.020 0.015 0.002 0.000 -0.005 -0.001 -0.004 -0.008 0.000 0.000 -0.001 0.001 -0.004 -0.004 -0.001
Hardw 0.021 0.011 0.004 -0.007 -0.010 0.000 -0.014 -0.013 0.000 -0.002 -0.002 0.001 -0.007 -0.007 -0.001
Softw 0.021 0.012 0.004 -0.006 -0.010 0.000 -0.016 -0.012 -0.001 0.001 0.001 0.001 -0.007 -0.006 -0.002
Chips 0.022 0.014 0.003 -0.001 -0.001 0.000 -0.009 -0.007 0.001 -0.002 0.000 0.002 -0.006 -0.003 0.000
LabEq 0.023 0.009 0.003 0.003 -0.007 0.000 -0.005 -0.013 0.001 0.000 -0.005 0.001 -0.009 -0.008 0.000
Paper 0.012 0.011 0.000 0.002 -0.001 0.000 0.000 -0.003 0.000 -0.001 -0.001 0.000 -0.001 0.001 0.000
Boxes 0.014 0.007 0.001 -0.009 -0.011 -0.002 -0.002 -0.008 -0.001 -0.001 -0.007 -0.001 -0.001 -0.004 -0.001
Trans 0.017 0.016 0.001 0.005 0.000 -0.001 0.002 -0.003 -0.001 -0.003 0.001 0.001 -0.002 0.003 0.000
Whlsl 0.020 0.018 0.002 0.004 0.000 -0.001 -0.002 -0.002 -0.001 0.000 0.004 0.000 -0.002 0.000 0.000
Rtail 0.016 0.018 0.001 -0.001 -0.003 -0.002 -0.003 -0.003 -0.002 -0.001 -0.001 0.000 -0.001 0.000 -0.001
Meals 0.019 0.020 0.001 0.002 0.000 -0.001 0.000 -0.004 -0.001 -0.001 -0.001 0.000 -0.002 -0.002 -0.001
Banks 0.009 0.009 0.000 0.000 -0.004 -0.001 -0.003 -0.004 -0.001 -0.002 -0.003 0.000 -0.005 -0.006 0.000
Insur 0.010 0.010 0.000 0.002 -0.001 -0.001 -0.003 -0.003 -0.001 -0.001 -0.002 -0.001 -0.001 -0.003 -0.001
RlEst 0.020 0.019 0.002 0.002 -0.003 -0.002 -0.003 -0.002 0.000 -0.002 0.002 0.000 -0.005 -0.002 0.000
Fin 0.012 0.011 0.001 -0.002 -0.001 0.000 -0.005 -0.004 -0.001 -0.005 -0.003 0.001 -0.005 -0.001 0.000
Other 0.021 0.019 0.002 0.004 -0.001 0.000 0.001 0.000 -0.001 0.002 0.002 0.001 -0.004 -0.003 0.000
Positive 48 46 28 11 4 0 3 3 0 2 2 6 0 2 0
Negative 0 0 1 2 10 12 8 13 10 2 1 0 13 6 5
1 week 8 week 13 week 26 week 52 week
 37 
2.5.3 Economic significance of investor sentiment predictability 
Table 2.5 reports the economic significance of industry return predictability. The 
industry sentiment betas come from Equation 2.2, reported as the a1 regression 
coefficients in Table 2.4. The table reports industry returns attributable to a one standard 
deviation change in investor sentiment. For example, the AAII sentiment has an 18 
percent standard deviation Table 2.4 also reports that food has a 0.01 AAII sentiment 
beta at a one-week lag. Therefore, as Table 2.5 reports, a one standard deviation change 
in AAII sentiment at a one-week lag predicts 10 percent annual excess return for food. 
Table 2.5 reports annualized returns attributable to changes in the indicated sentiment 
measures and k-week lags. Bold indicates 10 percent or greater statistical significance 
estimated with White (1980) standard errors. 
The economic impact of sentiment on industries varies across measures, industries, 
and horizons. Economic significance is greatest for AAII sentiment and at one-week 
horizons. A one standard deviation change in the AAII survey, on average, results in 19 
percent annualized industry returns. Comparably, the II and BW measures are 13 
percent and 7 percent. Economic significance is greatest for shipping (0.28) and least 
for coal (-0.17) for the AAII and BW measures. Investor sentiment seemingly has the 
smallest economic effect on large-cap industries. Take, for instance, the economic 
impact of AAII sentiment on utilities (.05), banking (0.09), beer (0.11), and tobacco 
(0.12). There are notable exceptions, such as the large-cap drug industry (0.26), which 
question whether industry characteristics, such as capitalization, systematically attract 
investor sentiment. At longer horizons, the absolute value of economically significant 
predictability diminishes. For instance, at 52 weeks, the average economic impact of 
AAII sentiment drops from 19 to 4 percent. In absolute terms, the greatest economic 
significance at 52 weeks is drugs (-0.14) for AAII sentiment. The average economic 
significance of II and BW predictability at 52 weeks is negligible, at only negative one 
percent. Generally, economic significance only partially reverses over a 52-week 
horizon, although the results largely lack statistical significance. 
  
 38 
Table 2.5 Economic significance of investor sentiment predictability 
 
Notes: Table 2.5 reports the economic significance of investor sentiment predictability of excess industry returns at 
different k-week lags. The analysis first uses Equation 2.2 to estimate investor sentiment betas. Next, the analysis 
calculates annualized excess industry returns attributable to a one standard deviation change in either the American 
Association of Independent Investors (AAII), Investors Intelligence (II), or Baker & Wurgler (BW) sentiment 
measures. Bold indicates 10 percent or greater statistical significance estimated with White (1980) standard errors. 
2.5.4 Bullish and bearish investor sentiment 
This section investigates whether there are differences in industry return predictability 
when investor sentiment is bullish or bearish. Differences may occur due to investor 
preferences or market frictions. Conventional wisdom holds that certain industries 
perform best during bull (bear) markets, characterized by high (low) investor sentiment. 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.20 0.17 0.03 0.13 0.06 0.02 0.00 0.02 -0.06 0.09 0.03 0.04 0.03 0.00 0.07
Food 0.10 0.09 0.03 0.02 -0.02 -0.06 0.00 -0.02 -0.02 0.01 0.02 -0.01 -0.03 -0.01 0.00
Soda 0.13 0.07 0.06 0.09 0.01 -0.09 0.06 0.03 0.00 -0.01 0.02 0.02 0.01 0.00 0.06
Beer 0.11 0.09 0.02 0.01 0.00 -0.03 0.01 0.03 -0.06 0.03 0.07 0.02 0.01 0.02 -0.03
Smoke 0.12 0.04 -0.08 -0.06 -0.09 -0.04 0.00 -0.07 0.00 0.04 -0.03 -0.10 0.04 0.00 -0.02
Toys 0.23 0.16 0.12 0.03 -0.04 -0.05 -0.03 -0.03 -0.02 0.00 -0.02 0.01 -0.08 -0.06 -0.08
Fun 0.24 0.17 0.10 0.03 -0.01 -0.05 0.02 -0.03 -0.03 0.02 0.00 0.00 0.01 0.02 -0.08
Books 0.16 0.10 0.09 0.01 -0.04 -0.06 -0.02 -0.04 -0.01 0.05 0.02 -0.01 -0.02 0.00 -0.06
Hshld 0.21 0.17 0.07 0.03 0.00 -0.03 -0.02 -0.02 -0.04 -0.04 -0.01 0.00 -0.02 -0.01 -0.02
Clths 0.21 0.21 0.11 0.00 0.00 -0.10 -0.03 -0.03 -0.04 -0.02 0.00 0.00 0.01 0.00 -0.03
Hlth 0.17 0.13 0.06 0.02 -0.07 -0.05 -0.03 -0.08 -0.05 -0.01 -0.01 -0.01 -0.07 -0.06 -0.02
MedEq 0.22 0.13 0.10 0.02 -0.05 -0.02 -0.06 -0.07 -0.06 -0.01 -0.02 0.00 -0.08 -0.08 -0.06
Drugs 0.26 0.16 0.10 0.02 -0.03 0.02 -0.09 -0.03 -0.03 -0.01 0.02 0.04 -0.14 -0.12 -0.08
Chems 0.13 0.10 0.04 0.02 0.00 -0.03 0.03 0.02 -0.02 -0.02 0.02 0.02 -0.03 0.02 -0.02
Rubbr 0.21 0.14 0.07 0.00 -0.02 -0.06 -0.02 -0.02 -0.01 -0.03 -0.01 -0.01 -0.03 0.00 -0.05
Txtls 0.19 0.14 0.11 0.01 -0.03 -0.07 0.01 -0.05 -0.05 -0.07 -0.04 0.01 -0.05 0.00 -0.04
BldMt 0.19 0.11 0.08 0.04 0.02 -0.03 0.03 0.00 0.00 -0.02 0.00 0.01 -0.01 -0.01 0.01
Cnstr 0.26 0.24 0.06 0.02 -0.01 0.00 -0.03 -0.01 -0.06 -0.02 -0.02 -0.02 -0.06 -0.04 -0.04
Steel 0.20 0.17 0.08 0.07 0.04 -0.05 0.00 0.04 -0.03 -0.03 0.07 0.08 -0.03 0.07 0.02
FabPr 0.18 0.08 0.07 0.02 -0.04 -0.06 -0.05 -0.06 -0.02 0.00 0.05 0.07 -0.06 0.01 0.03
Mach 0.18 0.13 0.06 0.04 0.00 -0.02 -0.03 -0.04 0.00 -0.02 0.01 0.02 -0.04 0.01 0.00
ElcEq 0.22 0.08 0.15 0.02 -0.06 0.04 -0.02 -0.06 0.05 -0.03 -0.04 0.03 -0.06 -0.04 0.02
Autos 0.22 0.15 0.09 0.01 -0.03 -0.06 0.00 -0.03 -0.05 -0.01 0.01 0.02 -0.04 -0.01 -0.01
Aero 0.23 0.20 0.09 0.06 0.04 -0.01 0.13 0.04 0.00 0.03 0.03 0.04 -0.04 0.02 -0.01
Ships 0.28 0.23 0.03 0.07 0.04 -0.04 0.00 0.06 -0.06 -0.08 0.03 -0.05 -0.04 -0.02 -0.01
Guns 0.22 0.15 -0.02 0.07 0.05 -0.06 0.06 0.05 -0.03 -0.01 0.02 -0.04 -0.03 0.01 0.02
Gold 0.10 0.22 -0.09 0.16 0.18 0.04 0.06 0.06 -0.08 -0.13 0.00 0.23 -0.03 -0.05 0.21
Mines 0.16 0.15 0.05 0.10 0.10 -0.03 0.01 0.07 0.00 -0.04 -0.01 0.07 -0.09 0.02 0.05
Coal 0.16 0.19 -0.17 0.15 0.12 -0.02 0.16 0.19 -0.04 0.11 0.08 0.17 -0.01 0.04 0.11
Oil 0.16 0.11 -0.04 0.17 0.09 0.05 0.09 0.08 0.07 -0.01 -0.01 0.09 -0.02 0.01 -0.01
Util 0.05 0.04 -0.02 0.02 0.02 0.01 0.03 0.04 0.01 0.02 0.02 -0.03 0.00 0.04 0.00
Whlsl 0.16 0.10 0.17 -0.06 -0.07 -0.04 -0.08 -0.09 0.07 0.00 0.00 0.09 -0.04 0.00 0.00
PerSv 0.20 0.15 0.10 -0.03 -0.02 -0.06 -0.07 -0.05 -0.06 -0.01 0.03 0.02 -0.06 -0.03 -0.01
BusSv 0.22 0.13 0.12 0.00 -0.04 -0.03 -0.04 -0.06 -0.01 0.00 -0.01 0.04 -0.04 -0.03 -0.04
Hardw 0.23 0.09 0.20 -0.07 -0.08 0.02 -0.13 -0.10 -0.02 -0.02 -0.02 0.05 -0.07 -0.06 -0.06
Softw 0.23 0.11 0.21 -0.06 -0.08 -0.02 -0.15 -0.09 -0.03 0.01 0.01 0.03 -0.07 -0.04 -0.10
Chips 0.24 0.12 0.18 -0.01 -0.01 0.02 -0.09 -0.05 0.05 -0.02 0.00 0.08 -0.06 -0.02 -0.01
LabEq 0.25 0.07 0.15 0.03 -0.06 0.01 -0.05 -0.10 0.06 0.00 -0.04 0.06 -0.08 -0.06 -0.02
Paper 0.13 0.09 0.02 0.02 -0.01 -0.01 0.00 -0.03 -0.02 -0.01 -0.01 0.02 -0.01 0.01 0.01
Boxes 0.15 0.06 0.05 -0.08 -0.09 -0.10 -0.02 -0.06 -0.03 -0.01 -0.05 -0.04 -0.01 -0.03 -0.05
Trans 0.19 0.13 0.06 0.05 0.00 -0.07 0.02 -0.02 -0.04 -0.03 0.01 0.05 -0.02 0.02 0.01
Whlsl 0.21 0.16 0.11 0.04 0.00 -0.04 -0.02 -0.01 -0.03 0.00 0.04 0.01 -0.02 0.00 -0.02
Rtail 0.17 0.16 0.06 -0.01 -0.02 -0.09 -0.02 -0.02 -0.08 -0.01 -0.01 -0.01 -0.01 0.00 -0.03
Meals 0.21 0.17 0.07 0.02 0.00 -0.04 0.00 -0.03 -0.05 -0.01 -0.01 0.01 -0.02 -0.01 -0.06
Banks 0.09 0.07 0.01 0.00 -0.03 -0.04 -0.03 -0.03 -0.04 -0.02 -0.02 -0.02 -0.05 -0.05 0.00
Insur 0.10 0.09 0.01 0.02 -0.01 -0.04 -0.02 -0.03 -0.04 -0.01 -0.01 -0.03 -0.01 -0.02 -0.03
RlEst 0.21 0.17 0.11 0.02 -0.03 -0.08 -0.03 -0.02 0.00 -0.02 0.02 -0.03 -0.05 -0.02 -0.02
Fin 0.13 0.10 0.07 -0.02 -0.01 -0.02 -0.05 -0.03 -0.04 -0.05 -0.02 0.04 -0.05 -0.01 -0.01
Other 0.23 0.16 0.11 0.04 -0.01 -0.03 0.01 0.00 -0.07 0.02 0.01 0.03 -0.04 -0.03 0.00
Positive 48 46 28 11 4 0 3 3 0 2 2 6 0 2 0
Negative 0 0 1 2 10 12 8 13 10 2 1 0 13 6 5
1 week 52 week26 week8 week 13 week
 39 
For instance, CNN Money reports that finance and technology shares are good bets as 
market sentiment ebbs in the later stage of a bull market.24 On the other hand, bearish 
sentiment favors the energy, health-care, and tech industries.25 Additionally, short-sale 
limitations restrict the ability of arbitrage investors to correct inflated values when the 
market is bullish (Gromb and Vayanos (2010)). Conversely, arbitrage investors face no 
restrictions taking the opposite side of deflated values during bear markets. The 
expectation is to observe greater investor predictability during periods of bullish 
sentiment than periods of bearish sentiment. 
Figure 2.2 Time-variant industry sentiment betas 
 
Notes: Figure 2.2 illustrates time-variant industry sentiment betas estimated using Investors Intelligence sentiment 
(II) data and 52-week rolling window beta estimations. Shaded areas indicate National Bureau of Economic Research 
defined periods of economic recession. 
Figure 2.2 illustrates industry return sensitivity to investor sentiment over time. 
Equation 2.2 estimates time-variant a1 coefficients over 52-week rolling windows. The 
rolling a1 coefficient estimates provide a measure of differences in time-variant industry 
exposure to investor sentiment. For clarity of illustration, the figure focuses on the 
                                                 
24 http://money.cnn.com/2011/03/04/markets/bull_market_sector_rotation/index.htm 
25 http://blogs.forbes.com/investor/2011/05/26/investors-favor-energy-health-care-and-tech/ 
-0.10
-0.05
0.00
0.05
0.10
0.15
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Industry Sentiment Betas
NBER Recessions Utilities Software Transportation
 40 
utilities, software, and transportation industries, as representative of defensive, cyclical, 
and neutral industries. The shaded area in Figure 2.2 indicates National Bureau of 
Economic Research (NBER) defined recessions. All three industries illustrate different 
cyclical exposure to investor sentiment. The cyclical software industry clearly has the 
most variable exposure to investor sentiment and the defensive utilities industry the 
least. Investor bullish and bearish sentiment, similarly, is pro-cyclical, as Figure 2.1 
illustrates. Industry exposure to sentiment thus potentially relates to cyclical bullish and 
bearish investor sentiments, which Equation 2.3 investigates. 
, 0 2 , , 3 , , 0 ,* *i t s t k s t k s t k s t k m t tR a a Sent Bull a Sent Bear b R e                  (Eq.2.3) 
Table 2.6 and Table 2.7 report industry return predictability for periods of bullish and 
bearish sentiment estimated with Equation 2.3. The equation runs a regression of excess 
industry returns (Ri) on a constant, investor sentiment (Sents) delineated by Bull and 
Bear dummy variables for the indicated k-week lags, and the market risk premium (Rm). 
The analysis defines bullish (bearish) sentiment for the AAII and II measures by a 
positive (negative) bull-bear spread. Brown and Cliff (2005) similarly delineate bull and 
bear markets. Positive (negative) BW index values define periods of bullish (bearish) 
sentiment. The bull dummy variables take a value of one during periods of bullish 
sentiment and zero otherwise. The construction of bear dummy variables is similar. 
Table 2.1 reports the number of bullish and bearish observations in the second and third 
columns. For instance, the AAII has 739 weeks of bullish sentiment and 328 weeks of 
bearish sentiment. The BW index, by construction, has roughly equal periods of bullish 
and bearish sentiment. Table 2.6 reports the a2 regression coefficients from Equation 
2.3, interpreted as industry return predictability during bullish periods. Table 2.7 reports 
the a3 regression coefficients from Equation 2.3, interpreted as industry return 
predictability during bearish periods. 
Table 2.6 and Table 2.7 documents three distinctions between investor sentiment 
predictability for bull and bear markets. First, statistically significant and positive 
predictability increases with bullish sentiment at an eight-week lag. AAII sentiment 
significantly predicts positive returns for 33 industries during bull markets, compared 
with AAII predictability of 11 industries for the full sample. Economic significance for 
all measures also increases at an eight-week lag. Conversely, significant predictability 
 41 
diminishes at long horizons. An interpretation is that bullish sentiment, particularly of 
small investors, causes short-term momentum and reversals. Secondly, positive 
predictability decreases at short horizons with bearish sentiment. The II survey now 
predicts positive returns for 22 industries at a one-week horizon and one industry at an 
eight-week horizon. At long horizons, negative predictability increases, compared with 
the full sample and bullish sentiment. The BW index now negatively predicts the returns 
of 31 and 23 industries at 8-week and 52-week lags, comparing with BW index 
predictability of twelve and five industries at similar horizons over the full sample. The 
evidence here suggests that investor sentiment results in greater price reversals when 
sentiment is bearish. Lastly, there is no clear difference in the effect of investor 
sentiment on cyclical and non-cyclical industries across periods of bullish and bearish 
sentiment. 
  
 42 
Table 2.6 Investor sentiment predictability during bull markets 
 
Notes: Table 2.6 reports the a2 slope coefficients from Equation 2.3. The equation runs a regression of excess industry returns on a constant, bull and bear sentiment dummy variables for the indicated k-week lags, and the 
market-risk premium. Sentiment measures are from the American Association of Independent Investors (AAII), 
Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates statistical significance of 10 percent or 
greater estimated with White (1980) standard errors. 
  
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.017 0.007 0.000 0.023 0.001 0.002 0.006 -0.007 -0.002 0.006 -0.005 0.000 0.009 0.006 0.004
Food 0.009 0.009 0.002 0.007 -0.002 0.000 0.003 -0.003 0.001 0.000 -0.001 0.000 -0.002 -0.006 0.001
Soda 0.015 0.013 0.001 0.018 0.005 -0.003 0.012 0.012 0.000 -0.005 -0.002 0.000 -0.007 -0.017 0.002
Beer 0.009 0.011 0.001 0.006 0.001 -0.001 -0.002 0.009 0.000 0.009 0.014 0.000 0.001 0.001 0.000
Smoke 0.007 0.004 0.002 -0.014 -0.005 0.000 0.000 0.003 0.001 0.007 -0.006 0.000 0.005 0.006 0.001
Toys 0.020 0.017 0.004 0.011 -0.004 0.002 -0.001 -0.015 0.001 0.006 -0.009 0.000 0.003 -0.006 0.001
Fun 0.019 0.016 0.003 0.006 -0.001 0.002 0.006 -0.008 0.000 0.003 -0.003 0.000 0.007 -0.005 0.000
Books 0.018 0.011 0.003 0.011 -0.009 0.001 0.001 -0.010 0.002 0.007 -0.001 0.000 -0.002 0.000 0.000
Hshld 0.020 0.017 0.002 0.009 -0.001 0.001 0.005 -0.011 0.000 0.001 -0.005 0.000 0.006 -0.003 0.001
Clths 0.015 0.022 0.003 0.008 0.002 0.000 0.004 -0.006 -0.001 0.005 -0.001 0.000 0.007 -0.002 0.001
Hlth 0.021 0.014 0.003 0.013 -0.005 0.001 0.002 -0.013 0.001 0.007 0.004 0.000 -0.001 -0.008 0.001
MedEq 0.026 0.015 0.003 0.011 -0.003 0.002 0.001 -0.012 -0.001 0.000 0.006 0.000 -0.002 -0.010 0.001
Drugs 0.031 0.016 0.003 0.009 -0.006 0.004 -0.004 -0.015 0.000 0.004 0.014 0.000 -0.008 -0.007 0.000
Chems 0.013 0.014 0.002 0.006 0.002 0.000 0.009 0.002 0.000 0.001 -0.003 0.000 0.000 0.002 0.000
Rubbr 0.015 0.016 0.002 0.007 -0.002 0.001 0.005 -0.009 -0.001 0.006 -0.005 0.000 0.000 0.003 0.000
Txtls 0.019 0.013 0.002 0.011 -0.005 0.000 0.009 -0.008 0.000 -0.004 -0.008 0.000 -0.002 0.002 0.004
BldMt 0.011 0.003 0.000 0.013 0.001 0.000 0.008 -0.005 0.000 0.005 0.000 0.000 -0.001 -0.001 0.002
Cnstr 0.024 0.031 0.002 0.018 -0.003 0.001 0.003 -0.012 0.000 0.003 -0.003 0.000 -0.002 0.002 0.002
Steel 0.014 0.025 0.000 0.016 0.011 0.000 0.005 0.006 -0.002 0.002 0.010 0.000 0.000 0.013 0.001
FabPr 0.018 0.009 0.001 0.007 -0.002 0.001 0.000 -0.015 0.000 0.000 0.011 0.000 -0.007 -0.004 0.001
Mach 0.015 0.018 0.002 0.009 0.004 0.001 0.001 -0.007 0.000 0.001 -0.001 0.000 0.000 -0.001 0.001
ElcEq 0.022 0.007 0.004 0.009 -0.007 0.003 0.000 -0.014 0.001 0.002 -0.011 0.000 -0.007 -0.005 0.000
Autos 0.018 0.020 0.001 0.011 -0.004 0.000 0.003 -0.010 0.000 0.004 -0.003 0.000 -0.001 -0.004 0.001
Aero 0.018 0.025 0.002 0.009 0.006 0.000 0.017 0.002 0.001 0.004 0.005 0.000 -0.009 0.001 0.001
Ships 0.015 0.022 0.001 0.019 -0.001 0.002 0.011 0.010 0.000 0.004 0.012 0.000 -0.005 -0.002 0.001
Guns 0.030 0.024 0.001 0.015 0.013 0.000 0.018 0.004 -0.001 0.006 0.004 0.000 0.007 -0.004 0.001
Gold -0.015 0.025 -0.001 0.014 0.016 0.004 0.007 0.000 -0.006 -0.015 -0.010 0.000 0.001 -0.018 0.010
Mines 0.004 0.024 0.001 0.016 0.008 0.001 0.006 0.017 -0.001 -0.001 -0.007 0.000 -0.001 0.005 0.003
Coal 0.016 0.030 0.002 0.025 0.021 0.001 0.019 0.024 0.000 0.024 0.013 0.000 0.003 0.001 0.004
Oil 0.010 0.012 0.002 0.017 0.010 0.003 0.014 0.007 0.002 0.003 0.001 0.000 -0.006 0.000 0.001
Util 0.005 0.005 0.001 0.004 0.002 0.000 0.004 0.004 0.001 0.005 0.004 0.000 -0.003 0.011 0.000
Whlsl 0.012 0.009 0.002 -0.002 -0.010 0.001 -0.006 -0.022 -0.001 0.001 -0.001 0.000 0.002 -0.003 0.000
PerSv 0.024 0.017 0.002 0.011 -0.001 0.000 0.006 -0.012 -0.002 0.001 0.004 0.000 -0.002 -0.006 0.000
BusSv 0.022 0.014 0.002 0.007 -0.008 0.002 0.000 -0.014 -0.001 0.003 0.001 0.000 -0.003 -0.005 0.000
Hardw 0.025 0.016 0.004 0.000 -0.010 0.005 -0.012 -0.018 -0.001 0.001 0.004 0.000 -0.003 -0.011 0.000
Softw 0.026 0.013 0.003 0.001 -0.012 0.003 -0.019 -0.022 -0.003 0.001 0.002 0.000 -0.003 -0.009 0.000
Chips 0.022 0.016 0.003 0.007 0.001 0.005 -0.009 -0.013 0.001 0.002 0.003 0.000 -0.004 -0.007 0.001
LabEq 0.024 0.009 0.004 0.008 -0.007 0.004 -0.005 -0.018 0.001 -0.001 -0.004 0.000 -0.006 -0.012 0.001
Paper 0.013 0.014 0.001 0.006 -0.003 0.001 0.001 -0.008 0.000 0.003 -0.005 0.000 0.000 0.005 0.002
Boxes 0.013 0.012 0.003 0.000 -0.009 -0.002 0.001 -0.010 -0.001 -0.001 -0.010 0.000 0.001 -0.007 0.001
Trans 0.018 0.014 0.002 0.011 -0.002 0.000 0.005 -0.009 -0.001 0.000 -0.002 0.000 0.001 0.005 0.001
Whlsl 0.021 0.020 0.002 0.013 0.002 0.001 0.005 -0.007 -0.001 0.002 0.004 0.000 0.003 -0.002 0.001
Rtail 0.014 0.014 0.002 0.007 -0.003 0.000 0.003 -0.004 -0.002 0.004 -0.005 0.000 0.003 -0.004 0.001
Meals 0.022 0.016 0.002 0.010 -0.004 0.001 0.007 -0.008 0.001 0.004 -0.003 0.000 -0.002 -0.003 -0.001
Banks 0.008 0.002 0.000 0.008 -0.011 0.000 0.005 -0.012 0.000 0.004 -0.006 0.000 0.003 -0.002 0.000
Insur 0.010 0.005 0.001 0.008 -0.004 0.000 0.004 -0.006 0.000 0.002 -0.002 0.000 0.000 0.000 -0.001
RlEst 0.017 0.016 0.003 0.014 -0.009 -0.001 0.008 -0.003 0.001 0.001 0.003 0.000 0.004 0.002 0.000
Fin 0.011 0.010 0.001 0.005 -0.003 0.001 -0.001 -0.012 -0.001 0.001 -0.009 0.000 -0.002 -0.001 -0.001
Other 0.020 0.015 0.003 0.008 0.000 0.001 0.001 -0.010 0.000 0.007 0.001 0.000 0.000 0.000 0.002
Positive 45 37 15 33 2 9 8 3 1 2 3 1 0 2 7
Negative 0 0 0 0 5 1 1 26 3 0 3 2 0 4 0
1 week 8 week 13 week 26 week 52 week
 43 
Table 2.7 Investor sentiment predictability during bear markets 
 
Notes: Table 2.7 reports the a3 slope coefficients from Equation 2.3. The equation runs a regression of excess industry returns on a constant, bull and bear sentiment dummy variables for the indicated k-week lags, and the 
market-risk premium. Sentiment measures are from the American Association of Independent Investors (AAII), 
Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates statistical significance of 10 percent or 
greater estimated with White (1980) standard errors. 
2.6 Control for Conditional Time-variant Market-risk Premium 
The analysis now investigates a rational expectations explanation of sentiment return 
predictability. The possibility exists that investor sentiment captures time-variant 
differences in the expected market-risk premium. For instance, rational investors may 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.023 0.052 0.001 -0.014 0.021 -0.001 -0.014 0.026 0.000 0.013 0.029 -0.001 -0.013 -0.015 -0.001
Food 0.013 0.015 -0.001 -0.009 -0.004 -0.002 -0.007 -0.001 -0.002 0.003 0.010 -0.001 -0.008 0.012 -0.001
Soda 0.008 -0.003 0.001 -0.014 -0.009 0.000 -0.008 -0.019 0.000 0.011 0.013 0.002 0.020 0.046 0.000
Beer 0.014 0.010 0.000 -0.010 -0.006 0.000 0.009 -0.009 -0.002 -0.009 -0.009 0.000 0.002 0.005 -0.001
Smoke 0.021 0.007 -0.005 0.014 -0.031 -0.002 -0.001 -0.040 -0.001 -0.003 0.001 -0.005 0.001 -0.016 -0.002
Toys 0.022 0.024 0.001 -0.016 -0.008 -0.004 -0.006 0.025 -0.001 -0.013 0.014 0.001 -0.035 -0.011 -0.004
Fun 0.027 0.030 0.001 -0.006 -0.003 -0.003 -0.009 0.008 -0.001 0.000 0.006 0.000 -0.013 0.020 -0.003
Books 0.008 0.015 0.000 -0.022 0.006 -0.003 -0.011 0.008 -0.002 0.000 0.012 -0.001 -0.004 0.000 -0.002
Hshld 0.017 0.025 0.001 -0.013 0.002 -0.002 -0.021 0.020 -0.001 -0.017 0.011 0.000 -0.021 0.006 -0.001
Clths 0.030 0.029 0.001 -0.020 -0.004 -0.004 -0.021 0.001 -0.001 -0.020 0.003 0.000 -0.014 0.006 -0.002
Hlth 0.003 0.017 0.000 -0.023 -0.020 -0.003 -0.018 -0.003 -0.003 -0.019 -0.017 -0.001 -0.021 -0.008 -0.002
MedEq 0.006 0.015 0.001 -0.022 -0.012 -0.003 -0.025 0.000 -0.001 -0.006 -0.024 0.000 -0.026 -0.010 -0.003
Drugs 0.006 0.023 0.001 -0.015 0.002 -0.003 -0.026 0.028 -0.001 -0.011 -0.027 -0.001 -0.031 -0.038 -0.003
Chems 0.013 0.008 -0.001 -0.008 -0.005 -0.001 -0.010 0.002 0.000 -0.009 0.015 0.000 -0.011 0.003 -0.001
Rubbr 0.031 0.016 0.001 -0.017 -0.006 -0.003 -0.020 0.013 0.000 -0.023 0.007 0.000 -0.010 -0.008 -0.002
Txtls 0.016 0.026 0.002 -0.022 -0.002 -0.003 -0.018 -0.005 -0.001 -0.014 0.005 -0.001 -0.015 -0.006 -0.005
BldMt 0.033 0.038 0.002 -0.018 0.004 -0.001 -0.011 0.011 0.000 -0.017 0.001 0.001 -0.002 -0.002 -0.001
Cnstr 0.023 0.018 0.000 -0.037 0.004 -0.001 -0.016 0.029 -0.002 -0.014 0.001 0.000 -0.017 -0.024 -0.003
Steel 0.030 0.006 0.003 -0.014 -0.011 -0.002 -0.013 0.002 0.000 -0.017 0.005 0.003 -0.012 -0.004 -0.001
FabPr 0.014 0.012 0.002 -0.010 -0.010 -0.003 -0.017 0.013 -0.001 0.002 -0.010 0.001 -0.005 0.012 0.000
Mach 0.020 0.010 0.001 -0.006 -0.012 -0.002 -0.013 0.002 0.000 -0.009 0.010 0.001 -0.014 0.007 -0.001
ElcEq 0.018 0.016 0.002 -0.014 -0.008 -0.001 -0.007 0.012 0.001 -0.015 0.009 0.001 -0.004 -0.004 0.000
Autos 0.026 0.013 0.002 -0.022 -0.001 -0.002 -0.006 0.013 -0.002 -0.012 0.011 0.001 -0.010 0.006 -0.002
Aero 0.028 0.017 0.001 0.001 0.002 -0.001 0.000 0.014 -0.001 0.001 0.002 0.001 0.006 0.007 -0.001
Ships 0.048 0.034 0.000 -0.024 0.019 -0.003 -0.028 0.002 -0.002 -0.037 -0.022 -0.002 -0.003 -0.002 -0.001
Guns -0.002 0.001 -0.002 -0.015 -0.012 -0.003 -0.023 0.014 0.000 -0.016 0.001 -0.004 -0.027 0.015 0.000
Gold 0.068 0.024 -0.002 0.016 0.031 -0.002 0.004 0.027 0.002 -0.011 0.025 0.000 -0.011 0.021 -0.003
Mines 0.043 0.000 0.000 -0.005 0.021 -0.002 -0.011 -0.013 0.001 -0.012 0.015 -0.001 -0.030 -0.003 -0.001
Coal 0.013 -0.002 -0.008 -0.011 -0.004 -0.002 0.005 0.014 -0.001 -0.020 0.001 0.003 -0.010 0.014 0.000
Oil 0.027 0.014 -0.003 0.013 0.012 -0.001 -0.003 0.017 0.000 -0.011 -0.006 0.002 0.008 0.004 -0.001
Util 0.005 0.006 -0.002 -0.001 0.006 0.000 0.002 0.006 -0.001 -0.007 -0.003 -0.001 0.006 -0.012 0.000
Whlsl 0.021 0.019 0.004 -0.018 -0.004 -0.002 -0.014 0.015 0.004 -0.003 0.004 0.003 -0.018 0.008 0.000
PerSv 0.007 0.017 0.002 -0.037 -0.009 -0.002 -0.042 0.010 -0.001 -0.006 0.003 0.000 -0.016 0.001 0.000
BusSv 0.016 0.019 0.002 -0.015 0.000 -0.003 -0.013 0.011 0.001 -0.007 -0.006 0.001 -0.007 -0.002 -0.002
Hardw 0.011 -0.003 0.003 -0.024 -0.011 -0.004 -0.018 -0.001 0.000 -0.010 -0.019 0.002 -0.019 0.004 -0.002
Softw 0.011 0.011 0.005 -0.023 -0.004 -0.003 -0.008 0.014 0.002 0.001 -0.003 0.001 -0.017 0.004 -0.003
Chips 0.022 0.009 0.003 -0.019 -0.009 -0.004 -0.009 0.009 0.001 -0.013 -0.009 0.002 -0.012 0.009 -0.002
LabEq 0.021 0.007 0.002 -0.010 -0.007 -0.003 -0.006 0.002 0.001 0.004 -0.007 0.002 -0.013 0.002 -0.002
Paper 0.010 0.003 0.000 -0.005 0.005 -0.002 -0.003 0.009 -0.001 -0.010 0.010 0.000 -0.003 -0.009 -0.001
Boxes 0.015 -0.006 -0.001 -0.029 -0.018 -0.002 -0.011 -0.002 0.000 -0.001 0.001 0.000 -0.005 0.004 -0.003
Trans 0.017 0.020 0.000 -0.010 0.003 -0.002 -0.003 0.013 -0.001 -0.008 0.007 0.001 -0.009 -0.003 -0.001
Whlsl 0.016 0.013 0.002 -0.017 -0.005 -0.002 -0.018 0.013 -0.001 -0.006 0.004 0.000 -0.015 0.004 -0.002
Rtail 0.018 0.030 0.001 -0.020 -0.003 -0.004 -0.015 0.001 -0.002 -0.012 0.007 -0.001 -0.012 0.012 -0.002
Meals 0.013 0.030 0.001 -0.018 0.008 -0.002 -0.015 0.009 -0.002 -0.013 0.005 0.000 -0.004 0.003 -0.002
Banks 0.010 0.027 0.000 -0.021 0.013 -0.001 -0.022 0.016 -0.002 -0.016 0.006 -0.001 -0.024 -0.017 0.000
Insur 0.009 0.024 -0.001 -0.014 0.006 -0.001 -0.019 0.004 -0.002 -0.008 0.000 -0.001 -0.004 -0.008 -0.001
RlEst 0.026 0.028 0.001 -0.027 0.011 -0.002 -0.030 0.000 -0.001 -0.010 0.000 -0.001 -0.027 -0.014 -0.001
Fin 0.015 0.015 0.002 -0.020 0.004 -0.002 -0.015 0.017 -0.001 -0.019 0.012 0.001 -0.013 -0.002 0.000
Other 0.023 0.028 0.001 -0.005 -0.004 -0.002 0.002 0.026 -0.003 -0.012 0.002 0.000 -0.013 -0.013 -0.002
Positive 33 22 11 0 1 0 0 9 1 0 4 2 0 3 0
Negative 0 0 4 25 1 31 18 0 11 11 2 0 18 4 23
1 week 8 week 13 week 26 week 52 week
 44 
require less compensation for market risk when sentiment is high and more when 
sentiment is low. As such, the industry return predictability previously documented may 
serve as a proxy for a market-risk premium, conditioned by prevailing investor general 
market sentiment. Baker and Wurgler (2006) similarly control for conditional market 
betas. After correcting for conditional market risk, if anything, predictability 
strengthens. Thus, the effect of investor sentiment on industry return predictability 
appears unrelated to a conditional market-risk premium. 
Table 2.8 reports the a1 regression coefficients from Equation 2.4. The a1 coefficients 
measure the predictability of industry returns after a correction for conditional market 
risk. Equation 2.4 runs a regression of excess industry returns (Ri) on a constant, 
investor sentiment at different k-week lags (Sentt-k), the market-risk premium (Rm), and 
an interaction term between the market-risk premium and investor sentiment (RmSentt-k). 
The interaction term effectively controls for the market-risk premium conditional on 
investor sentiment, following the methodology of Baker and Wurgler (2006, pg. 1673). 
Collectively, the a0 and a1 coefficient represent a decomposed Jensen’s alpha, modified 
with a conditional risk premium. Table 2.8 indicates in bold a1 coefficients that are 
statistically significant at 10 percent or greater using White (1980) standard errors. The 
bottom of Table 2.8 reports the total number of statistically significant positive and 
negative a1 coefficients. 
, 0 1 , 0 , 1 , ,i t s t k m t m t s t k tR a a Sent b R b R Sent e                   (Eq.2.4) 
  
 45 
Table 2.8 Interaction between investor sentiment and the market 
 
Notes: Table 2.8 reports the a1 coefficients from Equation 2.4. The equation runs a regression of excess industry returns on investor sentiment, the market-risk premium, and the interaction between investor sentiment and the 
market-risk premium for the indicated k-week lags. Sentiment measures are from the American Association of 
Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates 10 percent or 
greater statistical significance estimated with White (1980) standard errors. 
A time-variant risk premium fails to explain investor sentiment predictability. 
Significant predictability compares with the earlier results at a four-week horizon. 
Average economic significance does increase modestly. The most noticeable difference 
is for longer-term predictability and II sentiment. At a 52-week lag, II sentiment 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.021 0.021 0.001 0.010 0.000 0.001 -0.002 -0.005 -0.001 0.005 0.000 0.001 -0.001 -0.008 0.000
Food 0.012 0.012 0.001 0.000 -0.008 -0.001 -0.002 -0.008 0.000 -0.002 -0.001 0.000 -0.006 -0.007 -0.001
Soda 0.016 0.010 0.002 0.007 -0.006 -0.002 0.004 -0.003 0.000 -0.004 -0.002 0.000 -0.002 -0.008 0.000
Beer 0.013 0.013 0.001 0.000 -0.006 0.000 -0.001 -0.002 -0.001 0.001 0.005 0.000 -0.002 -0.004 -0.001
Smoke 0.014 0.007 -0.001 -0.008 -0.020 -0.001 -0.003 -0.015 0.000 0.001 -0.009 -0.002 0.001 -0.007 -0.001
Toys 0.024 0.021 0.003 0.000 -0.014 -0.001 -0.006 -0.012 0.000 -0.004 -0.007 0.000 -0.013 -0.018 -0.003
Fun 0.025 0.022 0.003 0.000 -0.011 -0.001 -0.002 -0.013 0.000 -0.003 -0.006 0.000 -0.003 -0.009 -0.003
Books 0.018 0.014 0.002 -0.001 -0.013 -0.001 -0.005 -0.013 0.000 0.001 -0.002 0.000 -0.006 -0.009 -0.002
Hshld 0.022 0.021 0.002 0.000 -0.009 0.000 -0.005 -0.010 0.000 -0.008 -0.006 0.000 -0.006 -0.010 -0.002
Clths 0.023 0.026 0.003 -0.003 -0.008 -0.002 -0.006 -0.012 0.000 -0.007 -0.005 0.000 -0.004 -0.010 -0.002
Hlth 0.019 0.017 0.002 0.000 -0.018 -0.001 -0.007 -0.019 0.000 -0.006 -0.007 0.000 -0.011 -0.017 -0.002
MedEq 0.023 0.017 0.003 -0.002 -0.015 0.000 -0.010 -0.018 -0.001 -0.006 -0.008 0.000 -0.013 -0.021 -0.003
Drugs 0.028 0.020 0.003 -0.003 -0.017 0.001 -0.015 -0.016 0.000 -0.008 -0.005 0.001 -0.022 -0.031 -0.004
Chems 0.016 0.014 0.001 -0.001 -0.009 -0.001 0.000 -0.006 0.000 -0.006 -0.003 0.001 -0.007 -0.007 -0.002
Rubbr 0.022 0.018 0.002 -0.002 -0.010 -0.001 -0.005 -0.010 0.000 -0.006 -0.006 0.000 -0.007 -0.008 -0.002
Txtls 0.021 0.018 0.003 -0.001 -0.012 -0.001 -0.001 -0.015 0.000 -0.010 -0.009 0.000 -0.009 -0.009 -0.002
BldMt 0.020 0.014 0.002 0.003 -0.005 -0.001 0.001 -0.007 0.000 -0.006 -0.005 0.000 -0.005 -0.010 -0.001
Cnstr 0.027 0.029 0.002 -0.001 -0.010 0.000 -0.006 -0.010 0.000 -0.007 -0.008 0.000 -0.011 -0.017 -0.002
Steel 0.023 0.022 0.002 0.004 -0.005 -0.001 -0.004 -0.005 0.000 -0.009 0.001 0.002 -0.009 -0.004 -0.001
FabPr 0.020 0.012 0.002 -0.001 -0.013 -0.001 -0.008 -0.015 0.000 -0.004 0.000 0.001 -0.010 -0.009 -0.001
Mach 0.020 0.018 0.002 0.001 -0.011 0.000 -0.007 -0.015 0.001 -0.007 -0.005 0.001 -0.010 -0.011 -0.001
ElcEq 0.024 0.011 0.004 -0.002 -0.017 0.001 -0.006 -0.017 0.002 -0.009 -0.012 0.001 -0.011 -0.017 -0.001
Autos 0.024 0.020 0.002 -0.001 -0.013 -0.001 -0.003 -0.012 0.000 -0.005 -0.004 0.000 -0.008 -0.012 -0.002
Aero 0.025 0.025 0.002 0.004 -0.004 0.000 0.010 -0.003 0.001 -0.001 -0.002 0.001 -0.009 -0.007 -0.002
Ships 0.028 0.027 0.001 0.004 -0.003 -0.001 -0.003 0.000 -0.001 -0.011 -0.001 -0.001 -0.008 -0.011 -0.001
Guns 0.022 0.019 0.000 0.004 0.000 -0.001 0.003 0.001 0.000 -0.004 -0.001 -0.001 -0.005 -0.005 -0.001
Gold 0.010 0.025 -0.002 0.014 0.019 0.001 0.006 0.006 -0.001 -0.015 -0.002 0.004 -0.003 -0.008 0.003
Mines 0.018 0.019 0.002 0.007 0.004 0.000 -0.002 0.001 0.000 -0.009 -0.006 0.001 -0.013 -0.006 0.000
Coal 0.019 0.023 -0.003 0.012 0.005 -0.001 0.012 0.013 0.000 0.006 0.004 0.003 -0.005 -0.007 0.001
Oil 0.018 0.014 0.000 0.013 0.003 0.001 0.006 0.002 0.002 -0.005 -0.007 0.002 -0.006 -0.008 -0.001
Util 0.006 0.006 0.000 0.001 -0.002 0.000 0.001 0.000 0.001 0.000 -0.001 -0.001 -0.002 -0.001 -0.001
Whlsl 0.019 0.014 0.004 -0.011 -0.021 -0.001 -0.013 -0.024 0.002 -0.008 -0.008 0.002 -0.011 -0.016 -0.002
PerSv 0.022 0.019 0.003 -0.006 -0.012 -0.001 -0.011 -0.014 -0.001 -0.005 -0.002 0.000 -0.010 -0.014 -0.001
BusSv 0.024 0.017 0.003 -0.003 -0.015 0.000 -0.008 -0.017 0.001 -0.006 -0.007 0.001 -0.009 -0.016 -0.002
Hardw 0.025 0.013 0.005 -0.013 -0.025 0.001 -0.019 -0.027 0.001 -0.011 -0.011 0.001 -0.015 -0.024 -0.003
Softw 0.026 0.015 0.005 -0.011 -0.024 0.000 -0.022 -0.026 0.001 -0.008 -0.009 0.001 -0.015 -0.023 -0.004
Chips 0.026 0.016 0.004 -0.006 -0.017 0.001 -0.015 -0.021 0.002 -0.011 -0.010 0.002 -0.014 -0.021 -0.002
LabEq 0.026 0.011 0.004 -0.002 -0.019 0.000 -0.010 -0.024 0.002 -0.007 -0.012 0.001 -0.015 -0.023 -0.002
Paper 0.015 0.013 0.001 0.000 -0.009 0.000 -0.003 -0.011 0.000 -0.004 -0.005 0.000 -0.005 -0.008 -0.001
Boxes 0.017 0.009 0.002 -0.012 -0.021 -0.002 -0.006 -0.017 0.000 -0.006 -0.013 -0.001 -0.006 -0.015 -0.002
Trans 0.021 0.018 0.002 0.002 -0.010 -0.001 -0.001 -0.012 0.000 -0.008 -0.005 0.001 -0.007 -0.008 -0.001
Whlsl 0.023 0.020 0.003 0.001 -0.009 -0.001 -0.005 -0.010 0.000 -0.005 -0.001 0.000 -0.007 -0.011 -0.002
Rtail 0.019 0.020 0.002 -0.004 -0.013 -0.002 -0.006 -0.013 -0.001 -0.006 -0.008 0.000 -0.006 -0.011 -0.002
Meals 0.022 0.022 0.002 0.000 -0.008 -0.001 -0.002 -0.011 0.000 -0.005 -0.005 0.000 -0.006 -0.010 -0.002
Banks 0.011 0.010 0.001 -0.002 -0.010 -0.001 -0.005 -0.010 0.000 -0.005 -0.006 0.000 -0.008 -0.012 -0.001
Insur 0.012 0.012 0.001 0.000 -0.009 -0.001 -0.005 -0.010 0.000 -0.005 -0.006 0.000 -0.005 -0.011 -0.002
RlEst 0.022 0.020 0.002 0.001 -0.009 -0.001 -0.005 -0.008 0.000 -0.005 -0.001 0.000 -0.008 -0.009 -0.001
Fin 0.015 0.013 0.002 -0.005 -0.009 0.000 -0.008 -0.011 0.000 -0.009 -0.008 0.001 -0.009 -0.010 -0.001
Other 0.025 0.021 0.003 0.001 -0.011 0.000 -0.002 -0.009 -0.001 -0.003 -0.004 0.001 -0.009 -0.014 -0.001
Postive 48 44 35 2 1 0 1 0 4 0 0 5 0 0 0
Negative 0 0 0 4 37 7 12 35 0 10 10 0 34 36 32
1 week 8 week 13 week 26 week 52 week
 46 
negatively predicts the returns for 36 industries, compared with six industries 
previously. The absolute value of economic significance noticeably increases at longer 
horizons, for all sentiment measures. For instance, at a 52-week lag, AAII sentiment 
predicts average industry performance of -0.008, double that previously observed. An 
increase in the statistical and economic significance of negative predictability provides 
some evidence of a time-variant risk premium. However, the results reported in Table 
2.8 support the view that investor sentiment predictability reflects only a limited 
component of compensation for conditional market risk. As such, the previous results, 
showing positive short-term and negative long-term predictability, continue to hold. 
However, a correction for a time-variant risk premium provides no better understanding 
of the industries that are most susceptible to speculative mispricing. 
2.7 Interaction of Industry Characteristics with Investor Sentiment 
This section takes several different approaches to investigate whether industries that 
share certain characteristics attract investor sentiment. The prior results indicate that the 
effect of investor sentiment is market wide, affecting the performance of most industries 
without distinction. Prior research also establishes that certain characteristics, which 
make objective valuations difficult, lead to speculative demand. For instance, Baker and 
Wurgler (2006) identify capitalization, valuation and dividend payout as stock 
characteristics that attract investor sentiment mispricing. Baker and Wurgler (2006) 
argue that valuation difficulties provide a channel for speculative behavior to drive 
prices from fundamentals, especially during periods of extreme investor sentiment. In a 
similar way, industries grouped by certain characteristics are potentially more subject to 
mispricing than are others. For instance, industries with low capital requirements tend to 
be more competitive, with less ability to control product prices.26 Consequently, the 
cash flows of highly competitive industries are relatively uncertain, which potentially 
leads to speculative mispricing. The analysis considers characteristics that broadly relate 
to industry competition, structure, growth, momentum, and risk. For thoroughness, the 
analysis incorporates a number of different empirical approaches, to evaluate the effect 
of investor sentiment on industry returns. 
                                                 
26 See for example Hoberg and Phillips (2010). 
 47 
2.7.1 Industry characteristics 
The literature, such as Baker and Wurgler (2006) and Kumar and Lee (2006), identifies 
greater speculative demand for stocks characterized as difficult to value and costly to 
arbitrage. In a similar spirit, the analysis identifies industry characteristics that 
potentially attract speculative demand due to valuation difficulties. Specifically, the 
industry characteristics investigated are return momentum, return volatility, systematic 
market risk, valuation ratios, sales volatility, capitalization, number of constituent firms, 
and Herfindahl concentration measures. Industry characteristics data comes from 
different sources. Monthly data for industry firms, BE/ME ratios, and capitalization 
come from the Kenneth French website.27 Quarterly data for industry sales, book equity, 
and total assets come from Compustat. In order to match the frequency of investor 
sentiment measures, the analysis assumes that the monthly and quarterly reported data 
remain constant during the weeks included in each period. The following discussion 
motivates each industry characteristic. 
Industry standard deviations of returns and market betas provide two volatility 
measures. Barberis and Shleifer (2003), Peng and Xiong (2006), and Kumar (2009) 
argue that speculative demand in popular investment styles leads to more volatile 
returns. The analysis uses a 12-week rolling window estimation of industry standard 
deviations. A market beta estimated with a single-index model provides an additional 
measure of industry volatility, estimated over 26-week rolling windows. The expected 
relationship between investor sentiment and both industry standard deviations and 
market betas is positive. 
Momentum provides a measure of speculative industry mispricing. The literature 
describes momentum as short-term return continuation, unexplained by traditional asset-
pricing models. Momentum, generally, remains an unsolved market anomaly, despite 
extensive literature on the topic.28 However, Moskowitz and Grinblatt (1999) argue that 
industry momentum largely explains stock-return momentum. Investor herding in 
popular industries potentially leads to predictable return momentum driven by investor 
                                                 
27 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
28 See, for instance, Fama and French (2008). 
 48 
sentiment.29 The analysis uses 12-week rolling windows to estimate industry 
momentum, which has an expected positive relationship with investor sentiment. 
The structure of an industry further determines its level of competitiveness. Average 
firm capitalization and the average number of constituent firms define industry 
structure. Hoberg and Phillips (2010) argue that industry structures determine cash flow 
volatility and analyst coverage. Peng and Xiong (2006) also discuss how a lack of 
analyst coverage in small-cap industries leads to informational inefficiencies. The 
analysis uses the natural log of industry market capitalization in millions of U.S. dollars. 
The number of constituent industry firms also proxies for industry competition. Industry 
competition is greater in industries characterized by a large number of small firms. The 
analysis uses the natural log of industry firms. The expectation is that investor sentiment 
has a negative relationship with industry capitalization and a positive relationship with 
the number of industry firms. 
Industry sales volatility and book-to-market valuation ratios (BE/ME) provide 
measures of industry growth potential.30 Baker and Wurgler (2006) argue that high-
growth firms face greater speculative price pressure, due to valuation uncertainty. Sales 
volatility characterizes uncertain industry profitability, such as for technology stocks in 
the dot.com market. Baker and Wurgler (2006) document a positive relationship 
between investor sentiment and sales volatility. Baker and Wurgler (2006) also provide 
evidence that investor sentiment has a greater effect on growth firms as characterized by 
small BE/ME ratios. The expectation is to observe that investor sentiment has a positive 
relationship with sales volatility and a negative relationship with BE/ME ratios. 
The Herfindahl index measures industry concentration, estimated as the sum of the 
squared market share for each firm in an industry.31 A lower Herfindahl score indicates 
greater industry competition. A recent empirical study by Hoberg and Phillips (2010) 
examines the predictability of industry boom-and-bust cycles related to industry 
competition. Hoberg and Phillips (2010) theorize that investor valuations fail to 
                                                 
29 Moskowitz and Grinblatt (1999). 
30 Baker and Wurgler (2006). 
31 See http://en.wikipedia.org/wiki/Herfindahl_index for an in-depth discussion of the construction and interpretation 
of Herfindahl measures. 
 49 
incorporate the effect of industry competition on distant cash flows. The study classifies 
stocks by their three-digit Standard Industrial Classification (SIC) code, with data from 
1972 to 2004. Hoberg and Phillips (2010) use the Herfindahl concentration measure to 
classify an industry as competitive or non-competitive. Hoberg and Phillips (2010) find 
that investors systematically neglect to assess the impact of competition on future 
industry performance. Therefore, inflated industry valuations result in predictable and 
economically significant industry reversals. Hoberg and Phillips (2010) also observe 
that stocks in competitive industries have upwardly biased analyst earnings estimates 
and high return co-movement. Hoberg and Phillips (2010) hypothesize that gathering 
information for competitive industries is costly. Consequently, investors rely on 
industry-specific rather than firm-specific information for valuations. The results 
reported in Hoberg and Phillips (2010) are robust to traditional risk corrections, leaving 
the possibility of a behavioral explanation linked to investor sentiment. Following Hou 
and Robinson (2006), the analysis uses three Herfindahl index measures, calculated as 
the sum of squared market share for industry sales, book equity and total assets. The 
expectation is for a negative relationship between Herfindahl concentration measures 
and investor sentiment. 
2.7.2 Non-parametric ranking of industry performance  
The examination begins with a simple non-parametric view of the performance of 
portfolios constructed from industries that share common characteristics.32 There are ten 
different portfolios, one for each industry characteristic. The numeric performance 
rankings for the 10 portfolios formed on industry characteristics therefore range from a 
low of one to a high of 10. Given there are 10 possible performance ranks, the average 
ranking is 5.5, which falls between a rank of 5 and a rank of 6. Therefore, a ranking 
below 5.5 indicates below average performance and a ranking above 5.5 indicates above 
average performance. To illustrate, industries grouped by HH sales have an average 
rank of 5 for the full sample. A rank of 5 indicates that the performance of industries 
grouped by HH sales characteristic is below average, when compared with the 
performance of all industry characteristic portfolios. 
                                                 
32 Baker and Wurgler (2006) take a similar approach in their analysis of firm-level characteristics. 
 50 
Table 2.9 reports the performance ranking for long-short portfolios formed on the decile 
sorts of the 10 different industry characteristics. The analysis first sorts the Fama and 
French 49 industries into decile portfolios from highest to lowest values – for each 
industry characteristic. Next, the analysis constructs long-short portfolios from these 
top-bottom decile groups for each characteristic. For instance, the momentum portfolio 
comprises a long (short) position in the five industries with highest (lowest) 12-week 
momentum. The objective of the long-short portfolios is to better measure differences in 
performance attributable to a particular shared industry characteristic. Panel A reports 
the average portfolio ranking across all 10 industry characteristic portfolios. Panel B 
reports the percentage of time that an industry characteristic portfolio’s performance 
ranks in the top 30 percentile. The table reports results for periods of bullish and bearish 
sentiment, and for comparison, the full sample. 
Table 2.9 reveals similar portfolio rankings across periods of bullish and bearish 
investor sentiment, for all sentiment measures and industry characteristics. This comes 
as a surprise. For instance, prior literature, such as Baker and Wurgler (2006), suggests 
higher returns for more volatile industries when sentiment is high, such as during 
periods of bullish sentiment. However, the performance of industries portfolios 
characterized by return risk and market risk is constant across all periods. The 
momentum portfolio provides a notable exception, with an average ranking above 8 for 
bull and bear markets. By construction, the momentum portfolios comprise the winning-
losing industries based on 12-month returns. This result thus indicates a continuation of 
past performance. The growth (BE/ME) and Herfindahl portfolios rank consistently 
below the average rank of 5.5, irrespective of bullish or bearish sentiment. The 
interpretation is that growth and competitive industries perform consistently above 
average. Nonetheless, there is no clear distinction for portfolio rankings between bullish 
and bearish sentiment. Panel B provides a different view. Here, the Herfindahl 
portfolios are the clear standouts. Average Herfindahl portfolio performance ranks in 
the top 30 percent approximately 15 percent of the time, independent of investor 
sentiment. Generally, however, the non-parametric analysis provides no guidance on the 
effect of investor sentiment on the returns of portfolios formed on common industry 
characteristics. 
  
 51 
Table 2.9 Performance ranking by industry characteristic 
 
Notes: Table 2.9 reports the return rankings for long-short decile portfolios formed on industries grouped by shared 
characteristics. Rankings range from a low of 1 to a high of 10. By construction, the mean rank is 5.5. The analysis 
first constructs long-short portfolios from the top decile-bottom decile sorts on industry characteristics. The industry 
characteristics considered are 12-week return volatility (stdev), 12-week return momentum (Mom), 26-week 
systematic market risk (Beta), number of firms in an industry (Firms), market capitalization (Size), book-to-market 
valuation ratios (BE/ME), Herfindahl sales (HH sales), Herfindahl book equity (HH equity), Herfindahl total assets 
(HH assets), and sales volatility (Sales ). The table reports results for the full sample, bull markets and bear markets. 
Positive (negative) bull-bear sentiment spread defines bull (bear) markets, for each sentiment measure. Panel A 
reports the relative return performance ranking across all 10 industry characteristics for the indicated periods. Panel B 
reports the percentage of time that the performance of each long-short characteristic portfolio ranks in the top 30 
percent across all characteristic portfolios for the indicated periods. 
2.7.3 Interaction between investor sentiment and industry characteristics 
The analysis now investigates the relationship between industry returns and the 
interaction between investor sentiment and industry characteristics, estimated with 
Equation 2.5. Similar in spirit to Baker and Wurgler (2006), the objective of this section 
is to evaluate whether investor sentiment has systematic effect on the returns of 
industries that share certain characteristics. As a point of difference, the focus of 
previous sections has been investor sentiment predictability of industry mispricing and 
long-term reversals. As such, the analysis now considers the contemporaneous 
relationship between industry characteristics and investor sentiment. The equation runs 
Full
Panel A: Sample AAII II BW AAII II BW
Stdev 5.4 5.5 5.5 5.4 5.2 5.1 5.4
Mom 8.1 8.2 8.1 8.3 8.1 8.3 8.0
Beta 5.4 5.5 5.4 5.5 5.4 5.5 5.4
Firms 5.4 5.5 5.4 5.6 5.1 5.4 5.0
Size 5.3 5.2 5.3 5.3 5.6 5.2 5.3
BE/ME 4.7 4.7 4.6 4.6 4.9 5.1 4.9
HH sales 5.1 5.0 5.1 5.1 5.1 4.8 5.1
HH equity 5.0 5.0 5.0 4.9 4.9 4.8 5.1
HH assets 5.1 5.0 5.0 5.0 5.1 5.2 5.2
Sales  5.4 5.4 5.4 5.3 5.4 5.3 5.4
Full
Panel B: Sample AAII II BW AAII II BW
Stdev 0.33 0.33 0.33 0.33 0.32 0.30 0.33
Mom 0.72 0.71 0.71 0.74 0.73 0.77 0.69
Beta 0.38 0.37 0.38 0.38 0.38 0.35 0.36
Firms 0.35 0.36 0.35 0.39 0.34 0.38 0.31
Size 0.24 0.22 0.24 0.24 0.27 0.24 0.23
BE/ME 0.21 0.21 0.21 0.19 0.22 0.23 0.24
HH sales 0.16 0.15 0.16 0.15 0.17 0.12 0.16
HH equity 0.16 0.16 0.17 0.15 0.16 0.14 0.17
HH assets 0.17 0.17 0.17 0.15 0.16 0.15 0.19
Sales  0.25 0.25 0.25 0.24 0.24 0.25 0.25
Bull Markets Bear Markets
Bull Markets Bear Markets
 52 
a regression of excess industry returns (Ri) on a constant, investor sentiment (Sents), 
industry characteristics (Charc), an investor sentiment and industry characteristic 
interaction term (SentsCharc), and the market-risk premium (Rm).33 All data is weekly 
and described in an earlier section. The variable of interest is the a3 regression 
coefficient, reported in Table 2.10. The table reports results for each sentiment measure 
and each industry characteristic. Bold indicates statistical significance of 10 percent or 
greater, estimated with White (1980) standard errors. The bottom two rows of Table 
2.10 report the number of significant positive and negative a3 regression coefficients. 
, 0 1 , 2 , 3 , , 0 ,i t s t c t s t c t m t tR a a Sent a Char a Sent Char b R e                 (Eq. 2.5) 
The results reported in Table 2.10 are inconclusive. Based on the literature, the 
expectation is for a positive investor interaction with industry standard deviation, 
momentum, beta, firms, and sales volatility characteristics. The expectation is for 
negative investor-sentiment interaction terms with size and the three Herfindahl 
competition measures. For instance, research shows that higher investor sentiment 
creates higher return volatility, which should result in a positive sentiment and standard 
deviation interaction term. Indeed, AAII and II sentiment indices have a significantly 
positive interaction with standard deviation for 23 and 20 industries. Industry firm 
numbers and capitalization also have the expected negative interaction with BW 
sentiment for 16 and 29 industries. Otherwise, statistical significance is not much more 
than expected by random chance, at 10 percent, or has the incorrect sign. 
  
                                                 
33 Equation 2.5 includes investor sentiment and industry characteristics as individual terms for correct model 
specification (Brambor, Clark, and Golder (2006)). 
 53 
Table 2.10 Interaction between investor sentiment and industry characteristics 
 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW 
Agric -0.174 0.684 -0.109 -1.018 -0.828 -0.093 0.016 0.002 0.013 0.009 0.027 0.004 -0.005 -0.012 0.000
Food 0.676 1.181 0.047 -0.130 0.314 0.083 0.013 -0.076 0.005 -0.007 -0.008 0.005 0.004 0.000 -0.002
Soda 0.872 -0.045 0.187 1.124 1.270 0.123 0.003 -0.016 0.007 0.001 -0.001 0.000 -0.006 0.004 -0.002
Beer 0.214 0.715 0.165 0.296 -0.142 0.014 -0.008 0.003 0.003 0.020 0.029 0.000 -0.002 -0.002 0.000
Smoke 0.036 -0.558 -0.012 -0.121 -0.994 -0.250 -0.028 -0.016 0.003 -0.043 -0.069 0.021 0.005 0.001 -0.003
Toys 1.074 1.889 -0.009 0.037 0.612 0.071 0.004 -0.050 0.001 0.022 0.048 -0.002 -0.002 -0.011 -0.003
Fun 0.716 1.447 -0.024 -0.135 0.341 0.068 0.027 -0.038 -0.001 0.006 0.023 0.001 -0.002 -0.003 -0.002
Books 1.213 1.626 0.000 0.043 0.381 0.098 0.026 -0.032 -0.006 -0.021 -0.035 0.006 0.013 0.015 -0.001
Hshld 0.984 0.995 0.012 -0.262 0.272 0.088 0.035 -0.020 0.004 -0.012 -0.009 0.005 0.003 0.001 -0.004
Clths 0.921 1.531 0.095 -0.281 0.474 0.161 0.013 -0.018 0.003 -0.006 0.017 0.005 0.000 -0.001 -0.004
Hlth 0.893 1.363 0.036 0.018 0.260 0.043 -0.025 -0.039 0.002 -0.022 -0.023 0.006 0.006 0.002 -0.003
MedEq 0.140 0.423 -0.114 -0.024 0.360 -0.038 -0.024 -0.063 0.002 0.012 0.026 -0.003 0.009 0.000 -0.003
Drugs -0.016 -0.211 -0.168 -0.104 0.473 -0.084 -0.024 -0.060 0.002 0.036 -0.017 -0.008 0.023 -0.004 -0.004
Chems 0.639 1.175 -0.031 -0.130 0.218 -0.008 0.010 -0.001 0.000 -0.024 -0.024 0.007 0.009 0.006 -0.004
Rubbr 0.798 1.861 0.063 -0.021 0.325 0.019 0.000 -0.067 0.003 -0.013 0.004 0.005 0.000 -0.014 -0.002
Txtls 1.611 2.146 0.026 -0.166 -0.172 0.074 0.005 -0.027 0.001 -0.009 -0.018 0.001 0.001 -0.001 0.000
BldMt -0.077 1.754 0.182 -0.692 -0.288 0.044 0.020 -0.011 -0.001 -0.030 -0.031 0.006 -0.001 -0.002 -0.004
Cnstr 1.073 0.637 -0.024 -0.316 0.508 0.075 0.015 -0.006 0.000 -0.015 -0.017 0.009 0.005 0.006 -0.003
Steel 0.592 0.776 0.006 -0.255 0.161 0.042 0.004 0.001 -0.001 -0.029 -0.020 0.004 0.002 0.020 -0.001
FabPr 0.288 0.807 -0.049 -0.233 0.407 0.010 -0.009 0.031 0.000 0.003 -0.016 0.000 -0.016 -0.004 0.003
Mach 0.660 1.148 -0.058 -0.232 0.020 0.029 0.011 0.015 -0.003 -0.018 -0.029 0.010 0.001 0.010 -0.003
ElcEq 0.490 1.020 -0.143 -0.006 0.086 -0.006 -0.023 -0.067 -0.006 -0.017 0.002 0.004 0.004 0.001 -0.001
Autos 0.956 1.655 0.093 -0.325 0.203 0.066 -0.005 -0.040 -0.001 -0.049 -0.036 0.000 -0.007 -0.015 -0.007
Aero 0.900 1.385 0.036 -0.269 0.000 0.011 0.015 0.025 -0.001 -0.048 -0.059 0.002 0.013 0.015 -0.001
Ships 0.875 0.105 0.120 -0.359 -0.331 0.170 0.028 0.023 0.001 -0.052 -0.018 -0.003 -0.004 -0.001 -0.003
Guns 0.123 -0.241 -0.213 -0.059 0.256 0.052 0.003 -0.008 0.009 -0.069 -0.095 0.009 0.028 0.008 -0.003
Gold -0.050 0.509 -0.284 -1.057 -0.230 -0.198 -0.002 -0.028 0.005 0.007 0.004 0.005 0.005 0.000 -0.003
Mines -0.737 -0.501 0.168 -0.444 -0.188 0.118 0.012 0.001 -0.003 -0.020 -0.013 0.008 0.008 0.012 -0.004
Coal -0.267 0.849 -0.241 0.077 0.087 0.010 -0.001 0.015 0.001 0.006 0.000 0.004 -0.001 0.010 -0.001
Oil -0.032 0.784 -0.110 -0.355 0.256 0.119 0.008 0.009 -0.003 -0.041 -0.060 0.010 0.009 0.013 -0.004
Util 1.244 0.960 0.058 -0.656 0.198 -0.111 -0.003 0.022 0.005 -0.018 -0.018 -0.001 0.004 0.003 0.000
Telcm -0.076 0.519 -0.022 0.018 0.660 0.006 -0.002 -0.009 0.001 0.017 0.011 0.000 0.008 0.006 -0.002
PerSv -0.359 0.986 -0.006 -0.086 -0.025 0.162 0.000 -0.035 0.002 -0.018 0.009 -0.010 0.006 0.000 -0.003
BusSv 0.745 1.077 -0.042 -0.233 0.060 0.037 0.018 -0.017 -0.001 0.049 0.077 -0.005 0.004 0.000 -0.002
Hardw 0.123 0.514 -0.004 0.384 0.525 -0.024 -0.024 -0.030 0.002 -0.017 0.010 0.001 0.011 0.005 -0.001
Softw 0.232 0.320 -0.013 0.308 0.582 -0.003 -0.010 -0.024 0.002 0.019 0.008 -0.001 0.005 -0.003 -0.001
Chips -0.012 0.183 -0.046 0.523 0.631 -0.047 -0.017 -0.028 -0.001 0.024 0.080 -0.002 0.004 0.000 -0.001
LabEq 0.304 0.494 -0.094 0.089 0.178 -0.044 -0.013 -0.056 -0.003 0.015 -0.003 0.005 0.002 -0.007 -0.003
Paper 0.729 0.640 -0.059 -0.247 0.339 0.046 0.005 -0.038 0.001 -0.015 -0.017 0.002 0.005 0.012 -0.002
Boxes 0.442 0.642 0.028 0.115 0.145 -0.204 -0.001 -0.006 -0.002 0.011 0.013 -0.002 -0.006 0.000 0.000
Trans 0.722 1.422 0.068 -0.357 -0.026 0.020 0.011 -0.015 0.002 -0.007 0.010 -0.003 0.004 0.001 -0.003
Whlsl 0.748 1.156 -0.058 -0.135 0.249 0.048 0.010 -0.018 -0.003 -0.017 -0.010 0.005 0.006 0.004 -0.003
Rtail 0.800 1.739 -0.052 -0.174 0.162 0.000 0.002 -0.031 -0.003 -0.020 0.032 -0.002 0.001 -0.001 -0.003
Meals 1.343 1.669 0.123 -0.302 0.198 0.057 0.003 -0.033 0.003 0.001 0.035 -0.001 0.005 -0.001 -0.004
Banks 0.272 1.045 0.079 0.086 0.615 0.108 -0.006 -0.050 0.005 -0.008 -0.026 -0.007 -0.007 -0.015 -0.003
Insur 0.529 0.552 -0.035 -0.203 0.019 0.095 0.000 -0.033 0.003 -0.009 -0.001 0.004 0.000 -0.005 -0.002
RlEst 0.615 0.718 0.143 -0.158 0.051 0.074 -0.013 -0.029 -0.002 0.001 0.021 0.007 -0.001 0.002 -0.003
Fin 0.066 0.550 -0.008 0.113 0.527 0.102 0.001 -0.026 0.002 -0.003 -0.001 0.000 0.000 -0.001 -0.001
Other 0.709 1.374 -0.058 -0.128 0.299 0.013 0.000 0.012 0.003 0.004 -0.006 0.011 0.000 0.001 -0.001
Positive 23 20 3 0 1 3 4 1 6 4 5 16 8 5 0
Negative 0 0 6 2 0 1 2 20 1 13 8 3 1 4 29
Stdev Mom Beta Firms Size
 54 
Continued: 
 
Notes: Table 2.10 reports the a3 coefficients from Equation 2.5, which runs a regression of excess industry returns on a constant, investor sentiment, industry characteristics, an interaction term of industry characteristics with investor 
sentiment, and the market-risk premium. Sentiment measures are from the American Association of Independent 
Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates 10 percent or greater 
statistical significance estimated with White (1980) standard errors. 
2.7.4 Regression of industry sentiment betas on industry characteristics 
The analysis now takes a different approach, investigating the relationship between 
industry sensitivity to sentiment and industry characteristics. To accomplish that, the 
analysis first uses Equation 2.2 to estimate industry betas (i,s) on the AAII, II, and BW 
sentiment measures, over the full sample period from 24/07/1987 to 28/12/2007. The 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW 
Agric 0.014 0.026 -0.003 0.031 0.074 0.044 0.023 0.108 -0.005 -0.020 0.068 -0.010 -0.002 -0.021 -0.003
Food -0.027 -0.108 0.007 0.444 0.129 0.025 0.100 0.055 0.008 0.095 -0.125 0.045 0.033 -0.016 0.012
Soda -0.012 -0.004 -0.003 0.011 0.027 0.026 0.004 -0.028 0.005 0.048 -0.092 0.009 -0.006 0.057 0.009
Beer -0.033 -0.030 0.001 -0.146 -0.211 0.022 0.000 -0.088 0.011 -0.070 -0.085 0.029 0.042 -0.018 0.006
Smoke 0.029 0.015 0.003 -0.052 0.012 0.021 -0.066 -0.022 0.018 -0.080 -0.025 0.022 0.014 -0.014 -0.005
Toys -0.021 0.074 0.013 0.012 0.089 0.003 -0.007 0.044 0.003 -0.003 0.046 -0.003 0.009 0.013 0.002
Fun 0.018 0.037 0.000 0.046 0.193 -0.012 0.040 0.224 0.025 -0.022 0.062 0.009 -0.045 -0.035 0.000
Books -0.055 -0.053 -0.003 0.324 0.329 -0.133 0.158 0.219 -0.054 0.177 0.163 -0.047 0.021 0.015 0.011
Hshld -0.007 -0.029 0.018 0.097 -0.214 0.079 0.046 0.051 0.006 0.068 -0.005 0.018 -0.024 0.061 0.023
Clths -0.082 -0.046 0.003 0.290 -1.306 0.174 0.093 -0.278 0.216 0.022 -0.878 0.233 -0.026 -0.070 -0.008
Hlth 0.015 0.031 -0.007 0.042 0.306 0.004 0.165 0.308 -0.018 0.159 0.325 -0.030 0.007 -0.075 0.009
MedEq -0.102 -0.099 0.013 0.866 -0.288 -0.170 0.523 0.169 -0.115 0.099 -0.244 -0.022 0.026 0.010 -0.002
Drugs -0.171 -0.228 0.024 1.017 -0.442 -0.129 0.187 -0.047 0.044 0.470 -0.177 -0.030 -0.100 -0.105 -0.022
Chems 0.002 0.016 0.005 -0.234 -0.118 0.039 -0.018 0.000 -0.018 -0.307 -0.334 0.061 -0.017 -0.068 0.010
Rubbr -0.027 0.177 -0.004 0.274 0.290 -0.073 0.060 0.183 -0.083 0.160 0.254 -0.096 -0.024 0.013 0.004
Txtls -0.009 0.054 0.002 0.020 0.049 -0.007 0.022 0.045 -0.005 0.011 0.026 -0.006 -0.029 -0.236 0.002
BldMt 0.044 -0.034 0.000 0.358 0.776 -0.064 0.491 0.643 -0.049 0.282 0.344 0.022 -0.012 0.004 0.008
Cnstr 0.017 -0.049 0.000 -0.339 0.162 0.202 0.005 0.815 0.068 -0.259 -0.238 0.053 -0.034 -0.015 0.005
Steel 0.018 -0.019 0.002 0.520 0.898 0.031 0.332 0.582 0.010 0.257 0.532 0.017 -0.024 -0.016 0.009
FabPr 0.033 0.024 0.001 -0.098 -0.196 0.014 0.001 0.155 -0.010 -0.107 -0.205 -0.004 -0.027 -0.053 -0.005
Mach -0.007 -0.045 0.015 -0.177 -1.966 0.264 0.082 0.025 0.076 -0.253 -0.665 0.222 -0.002 -0.072 0.002
ElcEq -0.010 -0.076 0.018 -0.043 0.164 -0.024 -0.056 0.286 0.018 -0.148 0.056 0.125 -0.052 -0.228 -0.010
Autos 0.026 0.049 0.002 0.109 0.193 -0.007 0.163 0.128 -0.008 -0.112 0.035 0.033 -0.008 -0.024 0.001
Aero -0.058 -0.062 0.006 0.268 0.346 -0.017 0.229 0.102 0.006 0.323 0.311 -0.029 0.030 0.018 0.001
Ships 0.052 0.035 0.023 0.054 0.041 -0.015 0.032 0.030 -0.016 0.042 0.039 -0.012 -0.063 -0.153 0.004
Guns -0.064 -0.035 0.000 0.055 0.035 -0.009 0.037 -0.041 -0.010 0.047 -0.011 -0.008 -0.042 -0.015 -0.008
Gold -0.131 -0.198 -0.006 0.011 0.076 -0.065 -0.012 0.416 -0.069 0.047 0.274 -0.082 0.131 0.077 -0.035
Mines -0.032 -0.030 0.010 -0.006 -0.229 0.075 0.024 -0.240 0.068 -0.019 -0.306 0.058 -0.032 0.002 -0.008
Coal -0.003 -0.023 0.003 0.011 -0.079 0.013 0.024 -0.028 0.003 0.031 -0.042 0.026 -0.020 -0.054 0.007
Oil -0.009 -0.027 0.010 0.190 0.498 -0.120 -0.705 0.739 -0.019 -0.974 1.270 -0.030 0.026 0.168 0.004
Util 0.010 0.010 0.003 0.654 1.099 0.026 1.524 2.615 -0.201 2.333 2.449 0.097 0.028 0.042 -0.007
Telcm 0.011 -0.024 0.008 -0.444 -0.768 -0.108 0.140 0.143 -0.042 -0.311 -0.084 -0.019 -0.101 -0.087 -0.023
PerSv -0.033 -0.034 -0.002 0.047 0.164 0.051 0.157 0.238 0.027 0.139 0.191 0.029 -0.002 0.010 0.005
BusSv -0.070 0.016 0.021 -0.313 -0.138 0.076 -0.227 0.217 0.106 -0.348 0.036 0.135 -0.006 -0.018 0.005
Hardw -0.064 -0.041 0.005 0.148 0.134 0.000 0.203 0.052 0.052 -0.317 -0.235 0.032 -0.042 0.010 0.005
Softw -0.025 0.000 0.013 -0.049 0.018 0.014 -0.025 0.034 0.008 -0.041 -0.002 0.009 -0.019 -0.019 0.000
Chips -0.007 0.027 0.005 -0.193 -0.008 0.071 0.361 0.584 -0.012 -0.030 0.044 0.023 -0.043 0.024 0.015
LabEq -0.025 0.020 0.005 -0.188 0.119 0.056 -0.047 0.149 -0.033 -0.182 0.074 0.044 -0.083 -0.209 0.002
Paper -0.008 -0.041 0.010 0.146 0.262 -0.042 0.158 -0.023 -0.039 0.210 0.336 -0.030 0.020 -0.064 -0.007
Boxes 0.024 0.009 0.000 -0.137 -0.049 0.003 -0.131 0.015 0.010 -0.022 0.038 -0.007 -0.070 -0.073 -0.002
Trans 0.008 0.038 -0.001 -0.434 -1.585 0.079 0.113 -0.438 0.085 -1.066 -0.580 0.282 0.020 -0.014 0.015
Whlsl -0.042 -0.032 0.030 0.262 0.386 -0.077 0.629 1.099 -0.024 0.286 0.429 -0.013 0.092 -0.014 -0.003
Rtail -0.002 -0.127 0.015 0.131 -0.348 -0.101 0.239 -0.592 0.002 0.006 -0.047 0.029 0.005 0.078 0.001
Meals -0.019 0.074 0.009 -0.124 -0.290 0.088 -0.051 -0.125 0.022 -0.102 -0.126 0.042 -0.045 0.026 0.012
Banks 0.023 0.032 0.010 -0.400 -0.744 -0.160 -0.411 -0.738 -0.200 -0.355 -0.726 -0.173 0.027 -0.053 0.011
Insur -0.006 0.039 0.015 0.065 0.222 0.134 -0.170 -0.468 -0.229 0.113 -0.151 -0.033 0.032 0.116 0.029
RlEst -0.008 -0.002 0.010 0.018 -0.139 0.010 0.064 -0.069 0.057 0.103 -0.011 0.011 -0.017 -0.004 -0.006
Fin 0.009 0.025 0.007 0.244 0.110 0.104 -0.099 0.005 0.030 0.221 0.085 0.054 -0.016 0.007 0.006
Other -0.061 -0.149 0.011 0.237 0.150 -0.018 -0.047 0.232 0.003 0.179 0.019 -0.057 0.003 0.018 0.002
Positive 5 6 15 10 9 11 9 12 8 10 6 16 2 4 3
Negative 7 6 0 3 7 9 2 3 7 7 2 4 2 7 3
BE/ME HH sales HH equity HH assets Sales 
 55 
analysis then uses Equation 2.6 to run a regression of industry sentiment betas on a 
constant and each industry characteristic (Charc). Bold indicates statistically significant 
coefficients at 10 percent or greater estimated with White (1980) standard errors. The 
table reports t-statistics directly beneath the a1 coefficients. 
Table 2.11 Regressions for industry sentiment betas 
 
Notes: Table 2.11 reports the a1 coefficients from Equation 2.6. The analysis first uses Equation 2.2 to estimate industry sentiment betas for the indicated investor sentiment measures over the full sample from 24/07/1987–
28/12/2007.The analysis  then  runs a cross-sectional univariate regression of industry sentiment betas on industry 
characteristic with Equation 2.6. The average industry characteristics evaluated are 12-week return volatility (stdev), 
12-week return momentum (Mom), 26-week systematic market risk (Beta), number of firms (Firms), market 
capitalization (Size), book-to-market valuation ratios (BE/ME), Herfindahl sales (HH sales), Herfindahl book equity 
(HH equity), Herfindahl total assets (HH assets), and sales volatility (Sales ). Sentiment measures come from the 
American Association of Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). 
The second column of Table 2.11 reports the expected sign of the a1 regression coefficients. The table reports White (1980) standard errors beneath the regression coefficients. Bold indicates statistically significant coefficients at 10 
percent or greater. 
, 0 1 ,s i c i ia a Char e                  (Eq. 2.6) 
Table 2.11 reports the a1 regression coefficients for the indicated sentiment measures 
and industry characteristics. The second column reports the expected sign of the a1 
coefficient. For instance, industry sentiment betas should positively increase with 
industry risk, as measured by standard deviation and a single-index beta. Indeed, the a1 
coefficient on beta is positive for all sentiment measures and statistically significant for 
Industry Expected
Characteristic Coefficient AAII II BW
Stdev positive coefficient 0.087 0.330 -0.077
t-statistic 0.56 3.28 -1.92
Mom positive coefficient 1.314 -0.077 0.405
t-statistic 1.75 -0.09 1.64
Beta positive coefficient 0.014 0.002 0.003
t-statistic 4.42 0.34 2.16
Firms positive coefficient 0.000 -0.001 0.001
t-statistic 0.36 -1.39 2.88
Size negative coefficient -0.002 -0.002 0.000
t-statistic -3.66 -2.45 -2.04
BE/ME negative coefficient -0.006 0.000 -0.002
t-statistic -1.35 0.04 -1.59
HH sales negative coefficient 0.001 -0.001 -0.003
t-statistic 0.16 -0.10 -3.89
HH equity negative coefficient 0.002 0.002 -0.004
t-statistic 0.29 0.21 -3.09
HH assets negative coefficient 0.001 -0.001 -0.003
t-statistic 0.25 -0.12 -2.61
Sales  positive coefficient -0.007 0.144 0.000
t-statistic -0.10 1.33 0.01
Investor Sentiment Betas ( s )
 56 
AAII and BW sentiment. The a1 coefficient, overall, shows the correct sign about 60 
percent of the time across all sentiment measures and industry characteristics. The a1 
coefficient for AAII and II sentiment generally lacks any statistical significance. 
However, results for BW sentiment are mostly significant and with the correct sign. 
Here again, the relationship between sentiment and industry characteristics appears to 
be weak and it varies substantially dependent on the measure of investor sentiment. 
2.7.5 Regression of industry characteristics on investor sentiment 
This section examines the interaction between investor sentiment and portfolios formed 
on decile sorts of industry characteristics. Similarly, Baker and Wurgler (2006) 
construct long-short portfolios to evaluate firm characteristics that are subject to 
investor speculation. Table 2.12 reports the a1 regression coefficients from Equation 
2.7. The interpretation of the a1 coefficients is the sensitivity of industry characteristics 
to investor sentiment. The analysis first constructs portfolios long (short) in the five 
industries in the top (bottom) decile for each industry characterization ( l sc cr r ). 
Equation 2.7 then runs a regression of the industry characteristic portfolios on a 
constant, the indicated investor sentiment measures (Sents), and the market-risk 
premium (Rm). The second column reports the expected sign of the a1 regression 
coefficients. The table reports results for both bull and bear markets, in comparison with 
the full sample. Bull (bear) markets are periods that have a positive (negative) bull-bear 
sentiment spread, for each sentiment measure. Bold indicates statistical significance at 
10 percent or greater estimated with White (1980) standard errors. 
  
 57 
Table 2.12 Regressions for industry characteristics 
  
Notes: Table 2.12 reports the a1 coefficients from Equation 2.7. The analysis first constructs long-short portfolios based on the top-bottom deciles from sorts on each different industry’s characteristics. The average industry 
characteristics evaluated are 12-week return volatility (stdev), 12-week return momentum (Mom), 26-week 
systematic market risk (Beta), number of firms (Firms), market capitalization (Size), book-to-market valuation ratios 
(BE/ME), Herfindahl sales (HH sales), Herfindahl book equity (HH equity), Herfindahl total assets (HH assets), and 
sales volatility (Sales ). The analysis next uses Equation 2.7 to estimate the a1 coefficients with a regression of long-short characteristic portfolios on a constant, the indicated investor sentiment measures, and the market-risk premium. 
The second column indicates the expected sign of the a1 regression coefficients. Sentiment measures come from the American Association of Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). 
The table reports results for the full sample, bull markets and bear markets. Postive (negative) bull-bear spreads 
define bull (bear) markets,for each sentiment measure. Bold indicates statistical significance at 10 percent or greater, 
estimated with White (1980) standard errors. 
, , 0 1 , 0 ,
l s
c t c t s t m t tr r a a Sent b R e                   (Eq. 2.7) 
Table 2.12 reports mixed results. The a1 coefficients have the correct sign 
approximately 56 percent of the time, for all measures and sample periods. However, 
the number of statistically significant coefficients with the correct sign drops 
dramatically to around 20 percent. For instance, industry capitalization and valuation 
(BE/ME) characteristics consistently have the correct sign, but mostly lack statistical 
significance. Overall, the BW index has the highest number of statistically significant a1 
coefficients with the correct sign, especially during periods of bearish sentiment. The 
results for AAII sentiment are overall the weakest. Generally, while the analysis 
indicates a link between industry characteristics and investor sentiment, the correlation 
is only weak, at best.  
2.7.6 Fama and MacBeth regressions for industry characteristics  
Lastly, the Fama and MacBeth (1973) regression analysis provides a final test of the 
interaction between investor sentiment and industry characteristics. First, Equation 2.8 
runs a regression of excess industry returns (Ri) on a constant, investor sentiment 
(Sents), industry characteristics (Charc), the interaction of investor sentiment with 
High - Low Decile Expected
Characteristic Portfolios Coefficient AAII II BW AAII II BW AAII II BW 
Stdev positive 0.017 0.003 -0.003 0.018 0.012 -0.003 0.045 -0.030 -0.004
Mom positive 0.035 0.037 -0.003 0.060 0.041 0.008 0.003 0.093 -0.013
Beta positive 0.007 -0.015 0.012 0.039 0.006 0.013 -0.091 -0.135 0.018
Firms positive 0.006 -0.036 0.015 0.018 -0.060 0.010 -0.026 -0.002 0.027
Size negative -0.017 -0.026 -0.003 -0.018 -0.026 -0.002 -0.041 -0.076 -0.006
BE/ME negative -0.027 -0.023 -0.006 -0.035 -0.016 -0.008 -0.043 -0.095 -0.009
HH sales negative 0.025 0.016 -0.004 0.037 0.025 0.000 0.026 0.010 -0.010
HH equity negative 0.025 0.023 -0.008 0.033 0.036 -0.003 0.035 0.011 -0.016
HH assets negative 0.014 0.007 -0.007 0.024 0.017 -0.004 0.003 -0.021 -0.015
Sales  positive -0.012 -0.003 -0.002 -0.006 -0.002 -0.005 -0.049 -0.009 0.001
Full Sample Bull Market Bear Market
 58 
industry characteristics (SentsCharc), and the market-risk premium (Rm). Equation 2.9 
then estimates cross-sectional i coefficients for each time period. The variable of 
interest is the coefficient.Table 2.13 reports the average  across bull and bear 
markets, in comparison with the full sample.The definition of bull (bear) markets are 
periods with a positive (negative) bull-bear sentiment spread, for each measure of 
investor sentiment. The second column reports the expected sign of the  coefficient. 
Bold indicates statistical significance at 10 percent or greater, estimated with Equation 
2.11. 
, 0 1 , 2 , 3 , , 0 ,*i t s t c t s t c t m t tR a a Sent a Char a Sent Char b R e               (Eq. 2.8) 
0 1 1, 2 2, 3 3, 4 0,i i i i i iR a a a b e                      (Eq. 2.9) 
3 3
1
1 T
tT                  (Eq. 2.10) 
3
3( ) /
t T

               (Eq. 2.11) 
The Fama and MacBeth (1973) results reported in Table 2.13 corroborate the earlier 
findings. The results document only limited evidence of industry characteristics that 
systematically attract investor sentiment-driven mispricing. Only 23 percent of the 
Fama and MacBeth (1973) coefficients are statistically significant and have the 
correct sign. Now, however, BW results show the weakest statistical significance, while 
AAII sentiment shows the strongest. In further contrast, results are now stronger during 
periods of bullish sentiment when estimated with the Fama and MacBeth (1973) 
regressions. Overall, the relationship between investor sentiment and industry 
characteristics lacks robustness across sentiment measures, samples periods, and 
estimations.  
 59 
Table 2.13 Fama and MacBeth coefficients for industry characteristics 
 
Notes: Table 2.13 reports the average 3 time series coefficients from Equation 2.10 following the methodology discussed in Fama and MacBeth (1973). Equation 2.8 first estimates time-series coefficients, regressing excess 
industry returns (Ri) on a constant investor sentiment (Sents), industry characteristics (Charc), the interaction of investor sentiment with industry characteristics (SentsCharc), and the market-risk premium. The average industry characteristics evaluated are 12-week return volatility (stdev), 12-week return momentum (Mom), 26-week 
systematic market risk (Beta), number of firms (Firms), market capitalization (Size), book-to-market valuation ratios 
(BE/ME), Herfindahl sales (HH sales), Herfindahl book equity (HH equity), Herfindahl total assets (HH assets), and 
sales volatility (Sales ). Equation 2.9 then estimates cross-sectional i coefficients for each period. Table 2.13 
reports the 3time-series averages estimated with Equation 2.10, for the full sample, bull markets, and bear markets. Periods of positive (negative) bull-bear spreads define bull (bear) markets, for each sentiment measure. Bold indicates 
t-statistics estimated with Equation 2.11 that are statistically significant at 10 percent or greater. 
The conclusion that ultimately emerges from the preceding analysis is that measures 
of industry characteristics only marginally explain the effect of investor sentiment on 
industry performance. This results contrast with previous firm-level studies that 
document a relationship between speculative mispricing and firm-level characteristics 
that cause valuation difficulties. It may be that the industry characteristics identified 
only provide a crude proxy for cash flow uncertainty. As such, they may fail to 
distinguish sufficiently industries that are most susceptible to speculative pricing. 
Alternatively, the effect of investor sentiment may represent a market-wide 
phenomenon, which extends to all industries. As a final test, the next section examines 
whether investor sentiment provides a practical signal that investors can use to 
profitably time industry investments. 
2.8 Investor Sentiment Strategy Returns 
Does investor sentiment provide the opportunity for profitable industry rotation? This 
section investigates a strategy that rotates industries with investor sentiment. Evidence 
of a profitable investor sentiment strategy would be interesting, not only from a 
practical perspective, but also from a market efficiency perspective. Predictable strategy 
performance would violate the efficient markets hypothesis, which states that returns 
follow a random walk and that prices reflect fundamental value. Both short-term 
mispricing and predictable long-term reversals are necessary for a profitable rotation 
Industry Expected
Characteristic Coefficient AAII II BW AAII II BW AAII II BW
Stdev positive 0.001 0.000 0.008 0.000 -0.001 -0.001 0.014 0.000 0.001
Mom positive 0.000 0.000 -0.001 0.000 0.001 -0.004 0.001 0.000 -0.003
Beta positive 0.035 -0.003 0.154 0.038 0.006 0.123 0.003 0.005 0.010
Firms positive 0.007 0.001 0.006 0.014 0.005 -0.028 -0.005 0.016 0.153
Size negative 0.357 0.117 -0.632 0.067 0.112 -0.575 0.003 -0.002 -0.429
BE/ME negative 0.004 0.004 0.080 -0.005 0.005 0.075 0.002 0.002 0.022
HH sales negative -0.001 -0.001 0.004 -0.001 -0.001 0.002 0.000 0.000 -0.002
HH equity negative -0.001 0.000 0.005 0.000 0.000 -0.003 -0.004 0.001 0.000
HH assets negative -0.001 -0.001 0.001 -0.002 -0.001 0.000 -0.001 0.000 -0.004
Sales  positive 0.011 0.003 -0.018 0.001 0.002 -0.004 0.004 0.000 -0.001
Full Period Bull Market Bear Market
 60 
strategy. While investor sentiment predicts short-term industry mispricing, the results 
provide limited evidence of significant long-term reversals. Nonetheless, the previous 
results show that industry sentiment alphas vary across periods of bullish and bearish 
sentiment. Time-variant industry exposure to investor sentiment suggests a profitable 
rotation strategy. Barberis and Shleifer (2003) and Peng and Xiong (2006) provide the 
theoretical basis for a style-rotation model. The Barberis and Shleifer (2003) and Peng 
and Xiong (2006) models theorize that profitable trading strategies result from shifts in 
investor style preferences, including industry styles. For robustness, the analysis 
investigates sentiment rotation strategies for different holding periods, risk corrections, 
and sub-periods. 
Table 2.14 reports returns for a strategy that rotates industry allocations based on their 
time-variant sentiment alphas. First, Equation 2.2 estimates industry a1 regression 
coefficients, estimated over 26-week and 52-week rolling windows. After allowing for 
the initial 26-week and 52-week alpha estimations, the first strategy holding periods 
start on 15/01/1988 and 18/07/1988. The interpretation of the a1 coefficients is the 
portion of industry excess returns, or alpha performance, attributable to time-variant 
investor sentiment. Next, the analysis constructs self-financing strategy portfolios (rl-rs), 
which are long (short) in the 15 of 49 industries with the lowest (highest) a1 regression 
coefficients. The table then evaluates strategy performance over different holding 
periods from four to 52 weeks. The strategy performance evaluation largely follows 
Moskowitz and Grinblatt (1999) and Brown and Cliff (2005). Those studies similarly 
construct and evaluate self-financing portfolios to evaluate strategy performance over 
different holding periods. Panel A and Panel B report strategy returns for portfolios 
formed on industry sentiment alphas, estimated over 26-week and 52-week rolling 
windows, for the indicated holding periods.34 Table 2.14 reports annualized Jensen’s 
alphas (J), Fama and French alphas (F), and Carhart alphas (C), estimated with 
Equations 2.12–2.14 to measure risk-adjusted strategy performance. Bold indicates 
                                                 
34 The study reports strategy results for non-overlapping holding periods. Unreported analysis also evaluates strategy 
returns with overlapping holding periods, similar to Brown and Cliff (2005). The use of overlapping periods results in 
overestimated statistical significance, which requires  correcting t-statistics following the methodology of Valkanov 
(2003). Results for overlapping regressions are quantitatively similar to non-overlapping regressions. 
 61 
statistical significance of 10 percent or greater estimated with White (1980) standard 
errors. 
Table 2.14 Investor sentiment strategy performance 
 
Notes: Table 2.14 reports annualised returns for an investment strategy that uses time-variant sentiment alphas to 
allocate industry investments. The analysis first uses Equation 2.2 to estimate time-variant industry sentiment alphas 
(a1) over 26- and 52-week rolling windows. Next, the analysis then constructs self-financing portfolios that are long 
(short) in the 15 lowest (highest) sentiment alpha industries. The table reports annualized Jensen’s (J), Fama and 
French (F), and Carhart alphas (C), estimated with Equations 2.12–2.14, for the indicated weekly holding periods. Panel A and Panel B report results for strategies based on 26-week and 52-week rolling window alpha estimations. 
Bold indicates statistical significance at 10 percent or greater estiamted with White (1980) standard errors. 
Panel A: Portfolios formed on sentiment alphas estimated with 26-week rolling regressions
04 Week 08 Week 13 Week 26 Week 52 Week
AAII Jensen's alpha 0.020 0.029 0.043 0.053 0.057
t-statistic 1.16 1.62 2.38 2.66 2.56
Fama & French alpha 0.007 0.009 0.023 0.024 0.021
t-statistic 0.41 0.52 1.29 1.33 1.03
Carhart alpha 0.010 0.024 0.033 0.023 0.014
t-statistic 0.54 1.33 1.75 1.14 0.63
II Jensen's alpha 0.007 0.005 ‐0.018 ‐0.016 0.007
t-statistic 0.40 0.25 ‐0.96 ‐0.74 0.32
Fama & French alpha ‐0.009 ‐0.013 ‐0.034 ‐0.041 ‐0.019
t-statistic ‐0.52 ‐0.69 ‐1.85 ‐2.09 ‐0.91
Carhart alpha 0.009 0.009 ‐0.016 ‐0.012 0.022
t-statistic 0.51 0.48 ‐0.88 ‐0.62 1.18
BW Jensen's alpha ‐0.024 ‐0.007 ‐0.017 0.004 0.068
t-statistic ‐1.07 ‐0.38 ‐0.83 0.26 3.79
Fama & French alpha ‐0.020 ‐0.007 ‐0.018 ‐0.016 0.044
t-statistic ‐0.98 ‐0.34 ‐0.95 ‐1.00 2.47
Carhart alpha ‐0.053 ‐0.027 ‐0.050 ‐0.023 0.035
t-statistic ‐2.57 ‐1.45 ‐2.48 ‐1.37 1.94
Panel B: Portfolios formed on sentiment alphas estimated with 52-week rolling regressions
04 Week 08 Week 13 Week 26 Week 52 Week
AAII Jensen's alpha ‐0.003 0.004 0.019 0.036 0.011
t-statistic ‐0.13 0.22 0.93 1.62 0.52
Fama & French alpha ‐0.028 ‐0.023 ‐0.010 ‐0.002 ‐0.022
t-statistic ‐1.62 ‐1.32 ‐0.58 ‐0.09 ‐1.19
Carhart alpha ‐0.010 ‐0.005 0.009 0.006 ‐0.019
t-statistic ‐0.57 ‐0.29 0.47 0.29 ‐0.94
II Jensen's alpha ‐0.043 ‐0.028 ‐0.035 ‐0.031 0.000
t-statistic ‐2.40 ‐1.53 ‐2.04 ‐1.85 ‐0.02
Fama & French alpha ‐0.057 ‐0.044 ‐0.049 ‐0.045 ‐0.024
t-statistic ‐3.11 ‐2.47 ‐2.77 ‐2.62 ‐1.35
Carhart alpha ‐0.044 ‐0.028 ‐0.037 ‐0.033 ‐0.001
t-statistic ‐2.41 ‐1.54 ‐2.07 ‐1.88 ‐0.03
BW Jensen's alpha ‐0.020 0.006 ‐0.002 0.041 0.033
t-statistic ‐1.03 0.30 ‐0.07 2.30 1.76
Fama & French alpha ‐0.035 ‐0.018 ‐0.004 0.019 0.034
t-statistic ‐1.89 ‐0.99 ‐0.21 1.08 1.78
Carhart alpha ‐0.062 ‐0.033 ‐0.036 0.003 0.002
t-statistic ‐3.28 ‐1.64 ‐1.75 0.16 0.13
 62 
, , 1 ,
l s
i t i t J m t tr r b R e                (Eq. 2.12) 
, , 1 , 2 3
l s
i t i t F m t t t tr r b R b SMB b HML e                (Eq. 2.13) 
, , 1 , 2 3 4
l s
i t i t C m t t t t tr r b R b SMB b HML b UMD e               (Eq. 2.14) 
Strategy performance varies substantially across all dimensions: portfolio formation, 
sentiment measures, and risk adjustment. Prior research, such as Fisher and Statman 
(2000), Brown and Cliff (2005), and Baker and Wurgler (2006), observes that investor 
sentiment-driven under-reaction (overreaction) leads to later momentum (reversals). As 
such, the expectation is to observe positive strategy returns for portfolios long (short) in 
industries with low (high) investor sentiment alphas. Portfolios formed on 26-week 
alpha estimations, as Panel A reports, show the least significant return performance. 
Overall, BW sentiment portfolios have the highest statistical and economic significance. 
For instance, with a 52-week holding period, the BW strategy generates a 6.8 percent 
Jensen’s alpha, which has a highly significant 3.79 t-statistic. Portfolios formed on AAII 
sentiment have positively significant Jensen’s alphas at longer horizons of 13–26 weeks. 
However, statistical significance dissipates for AAII sentiment after three-factor and 
four-factor risk corrections. Results for portfolios formed on 52-week alpha forecasts 
have the greatest overall statistical significance. However, with 52-week estimations, 
while II portfolio performance is now stronger, AAII portfolio performance is weaker. 
Interestingly, II portfolio performance is significantly negative, for all holding periods. 
Negative strategy performance indicates continued momentum for high-alpha 
industries, which the strategy portfolios hold short. 
Table 2.14 reports strategy performance before an allowance for transaction costs. 
Thus, inclusion of transaction costs would partially explain portfolio return 
performance. Transaction costs are both explicit and implicit; including commissions, 
bid-ask spread, and price impact. 35 Total transaction costs would also have varied 
greatly over the sample period from 1968 to 2007. An industry rotation strategy based 
on investor sentiment would have extremely high turnover and related transaction costs. 
                                                 
35 See, for example, Goyenko, Holden, and Trzcinka (2009). 
 63 
For instance, portfolios formed on 52-week alphas and updated every four weeks turn 
over approximately four times per annum. Round-trip transaction costs would amount to 
a minimum 4 percent a year, assuming modest transaction costs of 1 percent per 
turnover over the full sample. Of course, transaction costs decrease with an increase in 
holding periods. Portfolios held for 52 weeks, for instance, turn over on average once a 
year. 
Table 2.15 compares strategy return performance across two sub-periods. The AAII 
survey changed its sampling methodology from a direct mail survey to an online survey 
in 2000. Subsequent surveys, thus, potentially sample a different population of 
investors. Additionally, AAII and II surveys results are now readily available online. 
The consequent greater dissemination of the survey results has potentially limited their 
practical usefulness in recent years. The analysis considers two equal sub-periods. The 
first sub-period runs from 15/07/1988 to 24/04/1998 and the second from 01/05/1998 to 
01/02/2008. Sub-period analysis focuses on portfolios formed with sentiment alphas 
estimated over 52-week rolling windows. 
Strategy performance is more apparent in the first sub-period than in the second. In 
the earlier period, II sentiment portfolios are all statistically significant, regardless of the 
holding periods or the risk correction. However, as with the full sample, the II strategy 
again generates negative return performance. Statistical significance of the II strategy, 
with one exception, all but disappears in the later sub-period. The results are opposite 
for the BW strategy portfolios. Results for BW sentiment show greater statistical and 
economic significance in the later period. For instance, the BW portfolio has an 
annualized 8.2 percent Jensen’s alpha over a 52-week holding period. However, that 
performance decreases to a statistically significant 5.4 percent annual return with a four-
factor risk correction. The AAII portfolio performance is now statistically significant 
over long horizons in the early period and short horizons in the later period, whereas 
previously, performance was statistically insignificant for the entire period. 
 64 
Table 2.15 Sub-period strategy performance 
 
Notes: Table 2.15 compares annualized returns for an investment strategy that rotates industry investments with time-
variant sentiment alphas. The analysis first uses Equation 2.2 to estimate time-variant industry sentiment alphas 
estimated over 52-week rolling windows. Next, the analysis constructs self-financing portfolios that are long (short) 
the 15 lowest (highest) sentiment-alpha industries. The table reports annualized Jensen’s, Fama and French, and 
Carhart alphas from Equations 2.12–2.14 for the indicated holding periods. Panel A and Panel B report the indicated 
sub-period results. Bold indicates significance at 10 percent or greater estimated with White (1980) standard errors. 
Overall, timing industry allocations with investment sentiment measures do not 
produce systematic performance. Statistically significant performance for the full 
Panel A: Portfolios formed on sentiment alphas estimated with 52-week rolling regressions
Sample period -  15/07/1988 to 24/04/1998
04 Week 08 Week 13 Week 26 Week 52 Week
AAII Jensen's alpha 0.000 ‐0.002 0.014 0.010 ‐0.041
t-statistic 0.00 ‐0.13 0.74 0.51 ‐2.07
Fama & French alpha 0.015 0.010 0.025 0.020 ‐0.035
t-statistic 0.80 0.50 1.29 1.02 ‐1.73
Carhart alpha 0.018 0.011 0.025 0.017 ‐0.040
t-statistic 0.92 0.54 1.22 0.81 ‐1.86
II Jensen's alpha ‐0.059 ‐0.056 ‐0.065 ‐0.052 ‐0.047
t-statistic ‐3.34 ‐3.17 ‐3.64 ‐2.96 ‐2.61
Fama & French alpha ‐0.056 ‐0.058 ‐0.064 ‐0.055 ‐0.049
t-statistic ‐2.92 ‐3.09 ‐3.40 ‐2.99 ‐2.58
Carhart alpha ‐0.051 ‐0.058 ‐0.059 ‐0.050 ‐0.045
t-statistic ‐2.57 ‐2.97 ‐2.97 ‐2.63 ‐2.27
BW Jensen's alpha ‐0.017 ‐0.009 ‐0.012 0.011 ‐0.009
t-statistic ‐0.93 ‐0.53 ‐0.65 0.63 ‐0.47
Fama & French alpha ‐0.035 ‐0.028 ‐0.027 ‐0.005 ‐0.031
t-statistic ‐1.93 ‐1.60 ‐1.51 ‐0.25 ‐1.65
Carhart alpha ‐0.031 ‐0.025 ‐0.024 0.000 ‐0.036
t-statistic ‐1.61 ‐1.34 ‐1.24 0.00 ‐1.87
Panel B: Portfolios formed on sentiment alphas estimated with 52-week rolling regressions
Sample period - 01/05/1998 to 01/02/2008
04 Week 08 Week 13 Week 26 Week 52 Week
AAII Jensen's alpha ‐0.038 ‐0.019 ‐0.009 0.024 0.023
t-statistic ‐1.21 ‐0.59 ‐0.29 0.67 0.70
Fama & French alpha ‐0.066 ‐0.049 ‐0.040 ‐0.018 ‐0.014
t-statistic ‐2.53 ‐1.85 ‐1.49 ‐0.65 ‐0.51
Carhart alpha ‐0.048 ‐0.031 ‐0.021 ‐0.011 ‐0.010
t-statistic ‐1.84 ‐1.16 ‐0.78 ‐0.37 ‐0.35
II Jensen's alpha ‐0.041 ‐0.016 ‐0.016 ‐0.012 0.033
t-statistic ‐1.38 ‐0.55 ‐0.57 ‐0.44 1.03
Fama & French alpha ‐0.055 ‐0.031 ‐0.030 ‐0.026 0.010
t-statistic ‐1.90 ‐1.09 ‐1.08 ‐0.93 0.35
Carhart alpha ‐0.042 ‐0.013 ‐0.018 ‐0.014 0.034
t-statistic ‐1.46 ‐0.46 ‐0.65 ‐0.49 1.21
BW Jensen's alpha ‐0.007 0.024 0.027 0.068 0.082
t-statistic ‐0.21 0.73 0.78 2.31 2.75
Fama & French alpha ‐0.019 0.002 0.025 0.046 0.085
t-statistic ‐0.61 0.06 0.76 1.60 2.83
Carhart alpha ‐0.051 ‐0.015 ‐0.010 0.027 0.054
t-statistic ‐1.71 ‐0.48 ‐0.31 0.96 1.93
 65 
sample ranges, in absolute annual terms, from 3.1 to 6.8 percent. To put that 
performance in perspective, Moskowitz and Grinblatt (1999) estimate 12 percent annual 
returns for an industry rotation strategy based on return momentum. However, the 
performance documented varies substantially, dependent on the investor sentiment 
measure, risk correction, and sub-period. The results also document less statistically 
significant strategy performances in recent years, even before the inclusion of 
transaction costs. The results bring into doubt the practical application of using time-
variant investor sentiment to allocate industry investments. As such, one can question 
whether investors systematically price the announcement of sentiment surveys in 
industry returns, as examined next. 
2.9 Event Study with Extreme Bullish and Bearish Sentiment 
This section investigates industry response to the release of extreme sentiment survey 
measures. The earlier results show that, while investor sentiment positively predicts 
short-term industry mispricing, it provides limited predictability of price reversals. 
Moreover, a strategy that allocates industry investments based on time-variant investor 
sentiment generates only marginal and unsystematic outperformance. The results run 
contrary to the Barberis and Shleifer (2003) theoretical model and empirical findings of 
Jame and Tong (2009), among others. According to such studies, short-term mispricing 
should result in predictable long-term price reversals. There are, at least, two 
explanations for the results. The first is that short-term predictability, absent long-term 
reversals, indicates rational investor sentiment regarding industry values. Alternatively, 
short-term industry mispricing may gradually reverse over more extended periods. 
In the spirit of an event study, the analysis investigates industry response to the 
release of extreme investor sentiment news. Industry response to the release of AAII 
and II survey results indicates whether investors systematically price investor sentiment. 
To minimize the effect of noisy investor sentiment, and to accurately measure industry 
response, the analysis considers extreme measures of investor sentiment. Nofsinger and 
Sias (1999) and Lemmon and Portniaguina (2006) document that institutional and retail 
investor herding leads to mispricing. Moreover, extreme investor sentiment potentially 
captures a greater degree of investor herding. As such, extreme investor sentiment is 
more likely to have an observable and immediate effect on industry performance. 
 66 
Bullish or bearish sentiment that is one standard deviation above average defines 
extreme. There are roughly 200 weeks each with extreme bullish and bearish sentiment, 
for both the AAII and II sentiment measures. Interestingly, the correlation between 
extreme AAII and II bullish sentiment is relatively low at 35 percent, and lower yet for 
bearish sentiment at -17 percent. The analysis uses daily industry returns over the 
sample period 24/07/1987 to 28/12/2007, with data from the Kenneth French website.36 
Table 2.16 reports industry returns immediately following the announcement of 
extreme bullish or bearish investor sentiment. Equation 2.15 estimates industry response 
to bullish or bearish sentiment, with a regression of excess industry returns (Ri) on a 
constant daily dummy variables (dayN), and the market-risk premium (Rm). For bullish 
sentiment, the day0 dummy variables equals one on Thursday survey release days, when 
bull sentiment exceeds one standard deviation above average, and zero otherwise. The 
day1 and day2 dummy variables take the value of one on the first and second days 
subsequent to the release of extreme bullish sentiment and zero otherwise. Construction 
of the bearish sentiment dummy variables is similar.37 The analysis is limited to the 
AAII and II sentiment measures, which have weekly release dates whereas Baker and 
Wurgler (2006) construct their sentiment index from historical data and do not provide 
regular updates. Bold indicates statistical significance at 10 percent or greater estimated 
with White (1980) standard errors. The bottom two rows of the table report the total 
number of statistically significant positive and negative coefficients. 
, 0 0 1 2 0 ,0 1 2i t t t t m t tR a AR day AR day AR day b R e                         (Eq. 2.15) 
  
                                                 
36 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
37The release of AAII and II surveys occurs on Wednesdays. The study counts Thursday as the first full release day, 
or day0.  
 67 
Table 2.16 Event study results for extreme investor sentiment 
 
Notes: Table 2.16 reports industry returns in response to the release of investor sentiment measures greater than one 
standard deviation above average. Equation 2.15 estimates abnormal returns (AR) with a regression of excess 
industry returns on event-day dummy variables and the market-risk premium. Bull sentiment denotes investor 
sentiment greater than one standard deviation above average, and likewise for bear sentiment. Bold indicates 
statistically significant abnormal returns at 10 percent or greater estimated with White (1980) standard errors. The 
bottom rows report the total number of statistically significant positive and negative coefficients. 
Industry AR00 AR01 AR02 AR00 AR01 AR02 AR00 AR01 AR02 AR00 AR01 AR02
Agric 0.0014 0.0018 -0.0008 0.0005 0.0003 -0.0022 0.0009 0.0017 -0.0007 0.0008 -0.0006 -0.0017
Food 0.0009 0.0012 -0.0004 0.0002 0.0001 -0.0018 0.0007 0.0005 -0.0005 0.0003 -0.0001 -0.0023
Soda 0.0006 0.0020 -0.0007 -0.0014 -0.0011 -0.0016 0.0006 0.0008 0.0003 -0.0008 0.0009 -0.0034
Beer 0.0018 0.0012 0.0001 -0.0013 0.0006 -0.0004 -0.0003 0.0006 0.0006 -0.0013 0.0009 -0.0002
Smoke 0.0037 0.0006 -0.0012 -0.0007 0.0014 0.0001 0.0019 0.0004 0.0000 0.0002 0.0029 -0.0002
Toys 0.0021 0.0014 -0.0008 -0.0009 -0.0005 -0.0023 0.0016 0.0018 -0.0015 0.0000 0.0003 -0.0028
Fun 0.0016 0.0025 -0.0007 -0.0010 0.0006 -0.0027 0.0014 0.0022 -0.0005 -0.0011 0.0015 -0.0038
Books 0.0012 0.0011 0.0000 -0.0003 0.0005 -0.0020 0.0014 0.0000 -0.0002 -0.0014 0.0007 -0.0019
Hshld 0.0008 0.0014 0.0005 0.0004 -0.0004 -0.0017 0.0014 0.0013 0.0004 -0.0010 0.0002 -0.0025
Clths 0.0009 0.0018 -0.0004 0.0000 0.0000 -0.0030 0.0012 0.0013 0.0003 -0.0009 -0.0003 -0.0034
Hlth 0.0005 0.0021 0.0005 -0.0003 -0.0006 -0.0027 0.0002 0.0023 -0.0007 0.0001 0.0018 -0.0027
MedEq 0.0018 0.0026 0.0000 -0.0006 0.0006 -0.0024 0.0015 0.0021 -0.0010 0.0000 0.0013 -0.0029
Drugs 0.0022 0.0029 -0.0005 -0.0004 0.0012 -0.0026 0.0015 0.0025 -0.0015 0.0002 0.0012 -0.0029
Chems 0.0009 0.0008 0.0004 -0.0009 -0.0001 -0.0018 0.0010 0.0009 0.0006 -0.0007 -0.0005 -0.0026
Rubbr 0.0016 0.0026 -0.0008 -0.0007 -0.0008 -0.0025 0.0011 0.0015 0.0000 0.0000 -0.0001 -0.0021
Txtls 0.0004 0.0011 0.0004 0.0012 0.0006 -0.0019 0.0022 0.0005 0.0001 0.0000 -0.0003 -0.0024
BldMt 0.0014 0.0009 0.0002 -0.0010 -0.0009 -0.0018 0.0012 0.0005 0.0002 -0.0015 0.0003 -0.0016
Cnstr 0.0006 0.0022 -0.0001 0.0004 0.0004 -0.0028 0.0020 0.0017 0.0004 0.0013 0.0006 -0.0023
Steel 0.0012 0.0022 0.0000 -0.0015 0.0015 -0.0031 0.0018 0.0033 0.0002 -0.0008 0.0005 -0.0025
FabPr 0.0010 0.0021 -0.0001 -0.0027 0.0018 -0.0044 0.0011 0.0008 -0.0004 -0.0012 0.0013 -0.0031
Mach 0.0010 0.0013 0.0001 -0.0003 0.0000 -0.0020 0.0015 0.0017 0.0003 -0.0001 0.0003 -0.0024
ElcEq 0.0011 0.0017 0.0003 -0.0013 0.0004 -0.0021 0.0009 0.0012 0.0001 -0.0011 0.0015 -0.0028
Autos 0.0005 0.0011 -0.0001 -0.0001 0.0003 -0.0009 0.0011 0.0009 0.0003 -0.0014 0.0005 -0.0025
Aero 0.0012 0.0016 0.0003 -0.0006 -0.0008 -0.0029 0.0015 0.0010 0.0010 -0.0016 -0.0006 -0.0019
Ships 0.0019 0.0012 0.0021 -0.0009 -0.0010 -0.0027 0.0027 0.0010 0.0007 -0.0029 0.0004 -0.0009
Guns 0.0018 0.0031 0.0011 -0.0028 -0.0005 0.0001 0.0014 0.0014 0.0018 -0.0010 -0.0001 0.0001
Gold 0.0019 0.0044 -0.0001 -0.0015 0.0023 -0.0047 0.0009 0.0050 -0.0016 -0.0007 0.0043 -0.0055
Mines 0.0006 0.0019 0.0007 0.0002 0.0019 -0.0025 0.0021 0.0034 0.0010 -0.0004 0.0009 -0.0040
Coal 0.0013 0.0035 0.0034 -0.0004 0.0035 -0.0029 0.0034 0.0034 0.0015 -0.0009 0.0004 -0.0038
Oil -0.0002 0.0029 0.0004 -0.0019 0.0010 -0.0022 0.0017 0.0030 0.0000 -0.0007 0.0005 -0.0035
Util 0.0002 0.0006 0.0004 -0.0001 -0.0006 0.0003 0.0000 0.0006 0.0005 0.0003 -0.0002 -0.0003
Telcm 0.0000 0.0022 -0.0004 -0.0003 0.0007 -0.0018 0.0005 0.0019 -0.0006 0.0000 0.0014 -0.0021
PerSv 0.0014 0.0024 0.0002 -0.0012 0.0005 -0.0031 0.0010 0.0007 -0.0002 -0.0015 0.0022 -0.0040
BusSv 0.0015 0.0020 -0.0004 -0.0002 0.0002 -0.0032 0.0011 0.0015 -0.0009 -0.0002 0.0009 -0.0030
Hardw 0.0015 0.0021 -0.0004 -0.0004 0.0005 -0.0024 0.0010 0.0019 -0.0007 0.0011 0.0011 -0.0025
Softw 0.0012 0.0016 -0.0010 0.0000 0.0007 -0.0033 0.0009 0.0015 -0.0016 0.0008 0.0009 -0.0030
Chips 0.0018 0.0022 -0.0001 -0.0003 -0.0001 -0.0031 0.0013 0.0017 -0.0004 0.0009 0.0006 -0.0031
LabEq 0.0007 0.0024 0.0002 -0.0001 0.0003 -0.0031 0.0007 0.0020 -0.0002 -0.0004 0.0007 -0.0019
Paper 0.0009 0.0011 -0.0002 0.0002 0.0008 -0.0021 0.0010 0.0002 -0.0002 -0.0004 0.0017 -0.0018
Boxes 0.0019 0.0002 0.0014 0.0010 0.0020 -0.0017 0.0007 -0.0004 0.0006 0.0012 0.0008 -0.0010
Trans 0.0009 0.0016 -0.0006 0.0002 -0.0002 -0.0023 0.0009 0.0006 -0.0001 -0.0007 0.0003 -0.0018
Whlsl 0.0012 0.0025 -0.0001 -0.0006 0.0002 -0.0028 0.0013 0.0015 -0.0001 -0.0001 0.0008 -0.0028
Rtail 0.0002 0.0014 -0.0006 0.0007 -0.0004 -0.0015 0.0006 0.0007 -0.0003 -0.0005 0.0001 -0.0021
Meals 0.0011 0.0017 0.0001 0.0002 -0.0005 -0.0019 0.0009 0.0013 -0.0001 -0.0007 0.0005 -0.0026
Banks 0.0006 0.0005 -0.0002 -0.0002 0.0001 -0.0021 0.0003 0.0003 -0.0003 -0.0007 0.0004 -0.0017
Insur 0.0006 0.0009 0.0002 -0.0008 0.0012 -0.0012 -0.0002 0.0001 0.0003 -0.0005 0.0011 -0.0016
RlEst 0.0000 0.0024 0.0000 0.0003 -0.0011 -0.0023 0.0007 0.0020 -0.0010 -0.0022 0.0005 -0.0016
Fin 0.0007 0.0010 -0.0006 0.0005 0.0013 -0.0025 0.0007 0.0008 -0.0004 -0.0004 0.0001 -0.0023
Other 0.0019 0.0021 -0.0005 -0.0011 0.0000 -0.0021 0.0011 0.0025 -0.0008 0.0005 -0.0001 -0.0023
Positive 29 43 2 0 6 0 30 29 0 0 12 0
Negative 0 0 0 5 0 41 0 0 5 9 0 40
AAII Sentiment II Sentiment
Bullish Sentiment (+ Bearish Sentiment (+ Bullish Sentiment (+ Bearish Sentiment (+
 68 
Industry response to the announcement of extreme bullish and bearish sentiment is 
mostly significant with the correct sign. The results show significant and positive day0 
and day1 industry performance following the release of extreme AAII and II bullish 
sentiment measures. For instance, there are 29 and 43 industries with significant day0 
and day1 performance following extreme AAII bullish sentiment. The average AAII 
day1 response (.0018) shows slightly greater economic significance than day0 (.0011). 
Results for II sentiment are comparable with AAII sentiment. Notably, there is only 
weak evidence of statistically significant day2 reversals, for either measure. More 
interestingly, the results document an opposite effect following the release of extremely 
bearish sentiment, except with a two-day delay. There is only negligible evidence of 
day0 or day1 excess industry returns in response to extreme bearish sentiment 
announcements. However, there is a highly significant and negative day2 industry 
response for both measures. There are 41 and 40 industries with significantly negative 
day2 returns following the release of extreme bearish AAII and II sentiment, averaging 
-.0022 and -.0024 for each measure. The results indicate investors respond more quickly 
to extreme bullish sentiment than to bearish sentiment. Such results align with Hong, 
Lim, and Stein (2000), who similarly document that investors process bad news more 
slowly than good news. 
Generally, the immediate effect of extreme investor sentiment on industry 
performance is significant and widespread. Here again, the effect of sentiment appears 
to be market wide rather than industry specific. Investor response to extreme bullish and 
bearish sentiment suggests that investors believe that sentiment projects a continuation 
of prevailing market direction. This belief runs counter to prior market studies, 
including the industry analysis, which shows that initial investor sentiment-driven 
mispricing leads to predictable price reversals. To that end, excess returns following the 
announcement of extreme bullish and bear sentiment appear to reflect an element of 
rational industry performance expectations. 
2.10 Robustness Checks 
The results provide evidence that investor sentiment systematically predicts industry 
returns. However, the results are subject to potential criticisms. First, the sample period 
may drive the findings. A robustness test thus splits the sample and examines two equal 
 69 
sub-periods. Second, industry return predictability may simply reflect additional risk 
compensation. Therefore, another robustness test investigates investor sentiment 
predictability of risk-adjusted industry returns. The risk adjustment uses the well-known 
Fama and French three-factor and Carhart four-factor risk-correction models. Lastly, the 
results are potentially specific to the Fama and French industry portfolios. Therefore, as 
a further precaution, a robustness test examines return predictability using three 
alternative sector and industry classifications. Nevertheless, these additional results 
remain fundamentally unaltered, even after considering alternative sample periods, risk 
corrections, and industry classifications. 
2.10.1 Sub-period comparison 
The effect of investor sentiment on industry performance potentially varies across 
different sub-periods. Brown and Cliff (2005), for instance, show that the effect of 
investor sentiment on the market was greater prior to 1990. More recently, Brown and 
Cliff (2005) argue that the effect of sentiment on returns has declined due to a greater 
dissemination of investor sentiment measures. Thus, for robustness, the analysis 
examines two equal sub-periods, from 24/07/87 to 03/10/1997 and from 10/10/1997 to 
28/12/2007. 
Table 2.17 summarizes and compares the ability of investor sentiment to predict 
industry returns for the full sample with two sub-periods. The table compares results for 
bullish and bearish periods of sentiment. The table also reports the total number of 
statistically significant a1 regression coefficients from Equation 2.2. The a1 regression 
coefficients measure unconditional sentiment predictability of industry returns. 
Additionally, the table reports the total number of statistically significant a2 and a3 
regression coefficients from Equation 2.3. The a2 and a3 regression coefficients measure 
sentiment predictability of industry returns for periods of bullish and bearish sentiment. 
Table 2.17 reports the total number of statistically significant coefficients at 10 percent 
or greater estimated with White (1980) standard errors for the indicated investor 
sentiment measures at k-week lags. 
  
 70 
Table 2.17 Sub-period comparison of investor sentiment predictability 
 
Notes: Table 2.17 summarizes and compares the number of industries that investor sentiment predicts at 10 percent or 
greater statistical significance over the full sample and two equal sub-periods. The sub-periods are from 07/87 to 
10/97 and 10/97 to 12/07. The table reports the number of statistically significant a1 coefficients from Equation 2.2 and the statistically significant a2 and a3 coefficients for bullish and bearish sentiment periods from Equation 2.3. Sentiment measures are from the American Association of Independent Investors (AAII), Investors Intelligence (II), 
and Baker and Wurgler (BW). The analysis estimates statistical significance using White (1980) standard errors. 
Sentiment Sample
lag Measure Period positive negative positive negative positive negative
1 week AAII 1987-2007 48 0 45 0 33 0
1987-1997 43 0 33 0 20 0
1997-2007 46 0 40 0 32 0
II 1987-2007 46 0 37 0 22 0
1987-1997 42 0 12 0 39 0
1997-2007 44 0 43 0 21 0
BW 1987-2007 28 1 15 0 11 4
1987-1997 43 0 43 0 7 0
1997-2007 4 2 3 0 4 5
8 weeks AAII 1987-2007 11 2 33 0 0 25
1987-1997 2 3 9 0 1 4
1997-2007 16 0 32 0 0 18
II 1987-2007 4 10 2 5 1 1
1987-1997 2 8 0 22 10 0
1997-2007 5 4 15 0 0 32
BW 1987-2007 0 12 9 1 0 31
1987-1997 12 0 2 1 9 0
1997-2007 0 24 13 1 0 38
13 weeks AAII 1987-2007 3 8 8 1 0 18
1987-1997 2 18 1 1 0 18
1997-2007 6 2 10 1 0 1
II 1987-2007 3 13 3 26 9 0
1987-1997 1 7 0 8 8 0
1997-2007 4 10 2 17 13 0
BW 1987-2007 0 10 1 3 1 11
1987-1997 0 7 3 5 0 4
1997-2007 2 8 1 4 1 10
26 weeks AAII 1987-2007 2 2 2 0 0 11
1987-1997 1 2 1 0 1 12
1997-2007 1 0 2 0 1 3
II 1987-2007 2 1 3 3 4 2
1987-1997 15 1 12 2 0 2
1997-2007 2 3 2 5 0 1
BW 1987-2007 6 0 1 2 2 0
1987-1997 2 16 11 1 1 0
1997-2007 6 0 0 4 0 2
52 weeks AAII 1987-2007 0 13 0 0 0 18
1987-1997 0 11 8 1 1 21
1997-2007 0 5 0 1 0 3
II 1987-2007 2 6 2 4 3 4
1987-1997 1 5 3 8 2 4
1997-2007 3 1 1 1 2 1
BW 1987-2007 0 5 7 0 0 23
1987-1997 6 1 1 3 4 1
1997-2007 1 5 7 0 0 29
Bullish Sentiment Bearish SentimentFull Sample
 71 
The study’s general results remain largely unchanged for the two sub-periods. 
Investor sentiment systematically predicts returns for most industries at a one-week lag. 
AAII sentiment remains the best predictor of industry returns at a one-week horizon, 
followed by II, and then the BW sentiment measure. Again, predictability quickly 
diminishes for all measures, at longer horizons, especially at 26 and 52 weeks. For those 
horizons, statistical significance occurs only slightly more than expected by random 
chance, in the absence of true return predictability. Overall, the biggest change is 
substantially greater short-horizon and long-horizon predictability for BW sentiment 
during the earlier sub-period. Alternatively, AAII sentiment predictability is slightly 
more significant in more recent years, for all horizons, which runs contrary to the 
findings of Brown and Cliff (2005) for the market. 
2.10.2 Controls for risk factors 
This section examines whether investor sentiment predictability remains after 
controlling industry returns for additional risk sources. Baker and Wurgler (2006), for 
instance, provide evidence that sentiment affects the returns of small-cap and growth 
stocks. Prior research, such as Fama and French (1993), also identifies small-cap and 
growth premiums. As such, the investor sentiment predictability observed may merely 
represent expected investor compensation for bearing systematic risk. Equally, 
Moskowitz and Grinblatt (1999) identify industry returns as a source of momentum 
profits. The Carhart (1997) four-factor model identifies momentum as an additional 
source of systematic risk. The analysis controls industry returns for these well-known 
risk factors using the Fama and French (1993) three-factor and Carhart (1997) four-
factor risk-correction models. The three-factor model includes corrections for a size 
factor (SMB) and a growth factor (HML), in addition to the market. Additionally, the 
four-factor model includes a momentum factor (UMD). The size factor measures the 
return premium between small-cap and large-cap stocks. The growth factor measures 
the return premium between growth and value stocks. The momentum factor measures 
the return premium between high-return and low-return stocks. The SMB, HML, and 
 72 
UMD risk factors come from the Kenneth French website, where complete details are 
available on the construction of these risk factors.38  
, 0 1 , 0 ,i t s t k m t tR a a Sent b R e                  (Eq. 2.2) 
, 0 1 , 0 , 1 2i t s t k m t t t tR a a Sent b R b SMB b HML e                (Eq. 2.16) 
, 0 1 , 0 , 1 2 3i t s t k m t t t t tR a a Sent b R b SMB b HML b UMD e              (Eq. 2.17) 
Table 2.18 compares investor sentiment predictability of industry returns after 
corrections for different sources of systematic risk. The table summarizes the number of 
positive and negative a1 coefficient from single index (Eq. 2.2), Fama and French three-
factor (Eq. 2.16), and Carhart four-factor (Eq. 2.17) models, with statistical significance 
estimated with White (1980) corrected standard errors. Table 2.18 reports summary 
results for the indicated sentiment measure at a k-week lag and the indicated risk-
correction model. The table reports results for the full sample, bullish sentiment, and 
bearish sentiment. Additionally, Table 2.19 provides the complete results for investor 
sentiment predictability of industry returns corrected with a Carhart four-factor model 
over the full sample. 
  
                                                 
38 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
 73 
Table 2.18 Risk adjusted comparison of investor sentiment predictability 
 
Notes: Table 2.18 summarizes the number of positive and negative 1 regression coefficients statistically significant at 10 percent or greater. The table reports results for the indicated sentiment measures at various k-week lags, risk 
corrections, and periods of market sentiment. Sentiment measures come from the American Association of 
Independent Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). The analysis estimates risk-
corrected industry returns using the single-index (Eq. 2.2), Fama and French (1993) three-factor (Eq. 2.16), and 
Carhart (1997) four-factor (Eq. 2.17) models and statistical significance using White (1980) standard errors. 
Sentiment Risk 
lag Measure Correction positive negative positive negative positive negative
1 week AAII Single-index model 48 0 33 0 20 0
Fama & French 3-factor 34 0 23 1 8 0
Carhart 4-factor 32 0 25 1 5 2
II Single-index model 46 0 12 0 39 0
Fama & French 3-factor 19 0 13 1 9 1
Carhart 4-factor 19 0 10 1 9 2
BW Single-index model 28 1 43 0 7 0
Fama & French 3-factor 29 1 19 0 11 4
Carhart 4-factor 28 1 22 0 10 4
8 weeks AAII Single-index model 11 2 9 0 1 4
Fama & French 3-factor 2 10 2 1 1 17
Carhart 4-factor 2 4 5 1 1 14
II Single-index model 4 10 0 22 10 0
Fama & French 3-factor 4 17 2 11 1 5
Carhart 4-factor 3 14 2 11 1 5
BW Single-index model 0 13 2 1 9 0
Fama & French 3-factor 1 11 4 1 0 10
Carhart 4-factor 1 7 6 1 0 10
13 weeks AAII Single-index model 3 8 1 1 0 18
Fama & French 3-factor 2 7 3 1 0 18
Carhart 4-factor 2 6 6 1 0 16
II Single-index model 3 13 0 8 8 0
Fama & French 3-factor 3 22 3 28 10 0
Carhart 4-factor 3 18 4 27 10 0
BW Single-index model 0 10 3 5 0 4
Fama & French 3-factor 4 5 3 3 1 11
Carhart 4-factor 4 12 3 5 1 12
26 weeks AAII Single-index model 2 2 1 0 1 12
Fama & French 3-factor 2 3 1 0 0 8
Carhart 4-factor 2 4 1 0 0 6
II Single-index model 2 1 12 2 0 2
Fama & French 3-factor 2 11 2 12 5 5
Carhart 4-factor 1 11 2 15 5 4
BW Single-index model 6 0 11 1 1 0
Fama & French 3-factor 8 0 8 3 6 2
Carhart 4-factor 6 0 8 3 6 3
52 weeks AAII Single-index model 0 13 8 1 1 21
Fama & French 3-factor 0 8 2 2 0 13
Carhart 4-factor 0 11 2 3 0 11
II Single-index model 2 6 3 8 2 4
Fama & French 3-factor 0 14 0 21 6 2
Carhart 4-factor 0 15 0 24 8 1
BW Single-index model 0 5 1 3 4 1
Fama & French 3-factor 1 5 3 1 1 8
Carhart 4-factor 1 5 4 3 1 8
Bullish Sentiment Bearish SentimentFull sample
 74 
Table 2.19 Four-factor risk adjusted investor sentiment predictability 
 
Notes: Table 2.19 reports the a1 slope coefficients estimated with Equation 2.17. The equation runs a regression of industry returns on a constant investor sentiment at different k-week lags, and a Carhart (1997) four-factor risk 
correction. The Carhart (1997) four-factors are the market (Rm), size (SMB), value (HML), and momentum (UMD) risk premiums. Investor sentiment measures come from the American Association of Independent Investors (AAII), 
Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates 10 percent or greater statistical significance 
estimated with White (1980) standard errors. The bottom two rows report the total positive and negative significant 
coefficients. 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Agric 0.009 0.009 0.000 0.010 0.006 0.001 0.001 0.002 -0.001 0.008 0.001 0.001 0.004 -0.002 0.001
Food 0.003 0.004 0.000 0.000 -0.003 -0.001 0.000 -0.003 0.000 0.001 0.001 0.000 -0.003 -0.003 0.000
Soda 0.008 0.004 0.001 0.007 0.000 -0.002 0.005 0.003 0.000 -0.001 0.001 0.000 0.001 -0.001 0.001
Beer 0.006 0.007 0.000 0.000 -0.001 0.000 0.002 0.004 -0.001 0.003 0.007 0.000 0.001 0.002 -0.001
Smoke 0.006 -0.002 -0.002 -0.008 -0.013 0.000 -0.002 -0.009 0.000 0.004 -0.005 -0.002 0.003 -0.004 -0.001
Toys 0.010 0.008 0.002 0.002 -0.005 0.000 -0.001 -0.003 0.000 -0.001 -0.006 0.000 -0.007 -0.009 -0.001
Fun 0.010 0.008 0.002 0.001 -0.002 -0.001 0.003 -0.003 -0.001 0.001 -0.004 0.000 0.003 0.000 -0.001
Books 0.008 0.005 0.001 0.000 -0.005 -0.001 -0.002 -0.005 0.000 0.004 0.001 0.000 -0.002 -0.002 -0.001
Hshld 0.008 0.009 0.001 0.000 -0.001 0.000 -0.002 -0.002 -0.001 -0.005 -0.004 0.000 -0.001 -0.003 0.000
Clths 0.007 0.012 0.002 -0.002 0.000 -0.001 -0.003 -0.004 -0.001 -0.003 -0.003 0.000 0.002 -0.003 0.000
Hlth 0.004 0.003 0.001 -0.001 -0.010 -0.001 -0.002 -0.010 -0.001 -0.001 -0.005 0.000 -0.005 -0.010 0.000
MedEq 0.010 0.003 0.002 -0.001 -0.006 0.000 -0.003 -0.008 -0.001 -0.002 -0.006 0.000 -0.006 -0.010 -0.001
Drugs 0.013 0.006 0.002 0.000 -0.003 0.001 -0.005 -0.002 0.000 -0.001 -0.002 0.000 -0.010 -0.014 -0.001
Chems 0.001 0.001 0.000 -0.001 -0.001 0.000 0.003 0.002 0.000 -0.002 0.000 0.000 -0.003 0.000 0.000
Rubbr 0.008 0.004 0.001 -0.003 -0.004 -0.001 -0.002 -0.003 0.000 -0.003 -0.004 0.000 -0.002 -0.002 -0.001
Txtls 0.004 0.002 0.002 -0.002 -0.006 -0.001 0.000 -0.008 -0.001 -0.008 -0.007 0.000 -0.006 -0.005 -0.001
BldMt 0.006 0.001 0.001 0.002 0.001 0.000 0.003 0.000 0.000 -0.002 -0.003 0.000 -0.001 -0.004 0.000
Cnstr 0.008 0.012 0.001 -0.002 -0.003 0.001 -0.003 -0.001 -0.001 -0.003 -0.005 -0.001 -0.005 -0.010 -0.001
Steel 0.001 0.002 0.001 0.002 0.003 0.000 -0.001 0.004 0.000 -0.004 0.005 0.001 -0.003 0.003 0.000
FabPr 0.004 -0.003 0.001 -0.001 -0.005 -0.001 -0.004 -0.007 0.000 0.000 0.002 0.001 -0.005 -0.002 0.001
Mach 0.003 0.002 0.001 0.001 -0.001 0.000 -0.002 -0.005 0.000 -0.003 -0.002 0.000 -0.003 -0.001 0.000
ElcEq 0.008 -0.004 0.002 -0.001 -0.008 0.001 -0.001 -0.007 0.001 -0.004 -0.009 0.000 -0.004 -0.007 0.001
Autos 0.006 0.004 0.001 -0.002 -0.005 0.000 0.000 -0.004 -0.001 -0.001 -0.001 0.000 -0.004 -0.006 0.000
Aero 0.010 0.012 0.001 0.002 0.003 0.000 0.011 0.004 0.000 0.003 0.002 0.001 -0.004 0.000 0.000
Ships 0.012 0.013 0.000 0.002 0.003 0.000 -0.001 0.007 -0.001 -0.008 0.000 -0.001 -0.004 -0.005 0.000
Guns 0.012 0.008 0.000 0.003 0.004 -0.001 0.005 0.006 0.000 -0.001 0.001 -0.001 -0.003 -0.002 0.000
Gold -0.004 0.012 -0.002 0.009 0.018 0.001 0.005 0.006 -0.001 -0.014 -0.003 0.004 -0.002 -0.011 0.004
Mines 0.002 0.005 0.001 0.005 0.010 0.000 0.000 0.008 0.000 -0.005 -0.004 0.001 -0.009 -0.001 0.001
Coal 0.002 0.008 -0.004 0.007 0.011 0.000 0.013 0.020 0.000 0.011 0.007 0.003 0.000 0.002 0.002
Oil 0.000 -0.002 -0.001 0.009 0.008 0.001 0.006 0.008 0.002 -0.001 -0.004 0.002 -0.002 -0.003 0.000
Util -0.002 -0.001 0.000 -0.002 0.001 0.000 0.000 0.003 0.000 0.002 0.002 0.000 -0.001 0.001 0.000
Whlsl 0.006 0.002 0.003 -0.006 -0.007 0.000 -0.004 -0.010 0.001 -0.002 -0.004 0.001 -0.001 0.000 0.000
PerSv 0.009 0.007 0.002 -0.005 -0.004 -0.001 -0.007 -0.006 -0.001 -0.001 0.001 0.000 -0.005 -0.006 0.000
BusSv 0.010 0.004 0.002 -0.002 -0.006 0.000 -0.002 -0.007 0.000 -0.001 -0.005 0.000 -0.002 -0.005 0.000
Hardw 0.010 -0.001 0.003 -0.006 -0.008 0.001 -0.006 -0.011 0.000 -0.004 -0.008 0.000 -0.003 -0.005 0.000
Softw 0.012 0.002 0.003 -0.004 -0.008 0.000 -0.009 -0.010 -0.001 -0.001 -0.005 0.000 -0.002 -0.003 -0.001
Chips 0.011 0.002 0.003 0.000 0.000 0.001 -0.002 -0.004 0.001 -0.004 -0.006 0.001 -0.002 -0.001 0.001
LabEq 0.012 -0.003 0.002 0.002 -0.007 0.001 -0.001 -0.011 0.001 0.000 -0.009 0.001 -0.005 -0.007 0.000
Paper 0.002 0.001 0.000 0.000 -0.002 0.000 -0.001 -0.004 0.000 -0.001 -0.003 0.000 -0.001 -0.003 0.000
Boxes 0.003 -0.004 0.001 -0.012 -0.012 -0.001 -0.002 -0.008 -0.001 -0.002 -0.009 -0.001 0.000 -0.007 -0.001
Trans 0.004 0.002 0.001 0.002 -0.002 -0.001 0.002 -0.003 -0.001 -0.003 -0.002 0.001 -0.002 -0.001 0.000
Whlsl 0.008 0.006 0.002 0.002 0.000 0.000 0.000 -0.002 -0.001 -0.001 0.001 0.000 -0.001 -0.002 0.000
Rtail 0.004 0.007 0.001 -0.002 -0.004 -0.001 -0.002 -0.003 -0.002 -0.001 -0.004 -0.001 0.000 -0.002 0.000
Meals 0.010 0.010 0.001 0.000 -0.001 0.000 0.001 -0.004 -0.001 -0.002 -0.003 0.000 -0.001 -0.004 -0.001
Banks 0.000 0.000 0.000 -0.003 -0.005 0.000 -0.004 -0.005 -0.001 -0.002 -0.004 0.000 -0.005 -0.009 0.000
Insur 0.001 0.002 0.000 -0.001 -0.003 0.000 -0.004 -0.004 -0.001 -0.001 -0.003 -0.001 -0.002 -0.005 -0.001
RlEst 0.010 0.009 0.002 0.000 -0.004 -0.001 -0.003 -0.002 0.000 -0.002 0.000 -0.001 -0.004 -0.005 0.000
Fin 0.003 0.002 0.001 -0.004 -0.002 0.000 -0.005 -0.004 -0.001 -0.006 -0.005 0.001 -0.005 -0.003 0.000
Other 0.009 0.006 0.002 0.001 -0.002 0.000 0.003 0.000 -0.001 0.001 -0.002 0.000 -0.003 -0.005 0.000
Positive 32 19 28 2 3 1 2 3 4 2 1 6 0 0 1
Negative 0 0 1 4 14 7 6 18 12 4 11 0 11 15 5
1 week 8 week 13 week 26 week 52 week
 75 
The results provide evidence that well-known risk factors at least partially explain 
investor sentiment predictability, at both short horizons and long horizons. For instance, 
at a one-week horizon, statistically significant and positive AAII predictability drops 
from 48 to 34 for industries after a three-factor risk correction. The results are more 
striking for AAII positive predictability at a one-week lag for periods of bearish 
sentiment. Significant and positive predictability drops from 20 to five industries after a 
four-factor risk correction. More interestingly, the economic significance of investor 
sentiment predictability drops noticeably. For instance, AAII sentiment has an average 
a1 regression coefficient of 0.017 for industry returns corrected for the market at a one-
week lag. In comparison, the average a1 regression coefficient for AAII sentiment is 
0.006 for industry returns corrected with a four-factor model at a one-week lag. The 
statistical significance of risk-adjusted return predictability increases at longer horizons, 
especially for II sentiment and during periods of bullish sentiment. For instance, II 
sentiment significantly predicts four-factor adjusted returns for 24 industries at a 
52-week lag with bullish sentiment. That directly compares with the predictability of 
market-adjusted returns for eight industries. The absolute economic value of significant 
and negative predictability also increases for risk-adjusted returns at longer horizons. 
While short-term positive predictability decreases somewhat, the results for negative 
long-term reversals strengthen after three-factor and four-factor risk corrections. 
Consequently, the predictability of industry returns actually strengthens after correcting 
for well-known sources, particularly bullish II sentiment at all horizons. 
2.10.3 Alternative industry portfolios 
The choice of industrial classification scheme can determine the outcomes of empirical 
research (Bhojraj, Lee, and Oler (2003)). Additionally, the returns of industry portfolios 
comprising a few firms or a few dominant firms may merely represent idiosyncratic 
risk. The results may therefore misestimate the effect of investor sentiment 
predictability on industry values. For additional robustness, the analysis compares 
results for the Fama and French industries with results for alternative sector and 
industry groups. Specifically, the analysis maps the Fama and French 49 industry 
portfolios to the Kacperczyk, Sialm, and Zheng (2005) and GICS sector and industry 
classifications. Kacperczyk, Sialm, and Zheng (2005) map the Fama and French 48 
industry portfolios to one of 10 sector portfolios. Additionally, the analysis maps the 
 76 
extra software industry included in the Fama and French 49 industry classification to the 
Kacperczyk, Sialm, and Zheng (2005) business equipment and services sector. The 
analysis also uses the GICS 10 sector and 24 industry mappings. Analysts widely follow 
the GICS classification. Bhojraj, Lee, and Oler (2003) find that intra-industry return 
correlations are higher for the GICS classification than other popular industry 
classifications, such as the Standard Industrial Classification (SIC). Table 2.20 details 
the mapping of the original Fama and French 49 industry portfolios to the Kacperczyk, 
Sialm, and Zheng (2005) and GICS sector and industry portfolios. 
Table 2.20 Alternative sector and industry mappings 
 
Notes: Table 2.20 provides a mapping of the Fama and French 49 industry portfolios to Kacperczyk, Sialm, and 
Zheng (2005) 10 sector, GICS 10 sector, and GICS 24 industry portfolios. 
Kacperczyk, Sialm, Zheng 10 Sectors
Group Description Group Description Group Description Group Description
01 Agriculture 01 Consumer Non-Durable 30 Consumer Staples 3020 Food, Beverages, & Tobacco
02 Food Products 01 Consumer Non-Durable 30 Consumer Staples 3010 Food & Staples Retailing
03 Candy & Soda 01 Consumer Non-Durable 30 Consumer Staples 3020 Food, Beverages, & Tobacco
04 Beer & Liquor 01 Consumer Non-Durable 30 Consumer Staples 3020 Food, Beverages, & Tobacco
05 Tobacco Products 01 Consumer Non-Durable 30 Consumer Staples 3020 Food, Beverages, & Tobacco
06 Recreation 02 Consumer Durable 25 Consumer Discretionary 2520 Consumer Durables & Apparel
07 Entertainment 01 Consumer Non-Durable 25 Consumer Discretionary 2540 Media
08 Printing & Publishing 01 Consumer Non-Durable 25 Consumer Discretionary 2540 Media
09 Consumer Goods 02 Consumer Durable 25 Consumer Discretionary 2530 Consumer Services
10 Apparel 01 Consumer Non-Durable 25 Consumer Discretionary 2520 Consumer Durables & Apparel
11 Healthcare 03 Healthcare 35 Healthcare 3510 Healthcare
12 Medical Equipment 03 Healthcare 35 Healthcare 3510 Healthcare
13 Pharmaceutical 03 Healthcare 35 Healthcare 3520 Pharmaceuticals
14 Chemicals 04 Manufacturing 15 Materials 1510 Materials
15 Rubber & Plastic 04 Manufacturing 25 Consumer Discretionary 2550 Retailing
16 Textiles 01 Consumer Non-Durable 25 Consumer Discretionary 2520 Consumer Durables & Apparel
17 Construction Material 04 Manufacturing 15 Materials 1510 Materials
18 Construction 04 Manufacturing 25 Consumer Discretionary 2550 Retailing
19 Steel Works 04 Manufacturing 15 Materials 1510 Materials
20 Fabricated Products 04 Manufacturing 20 Industrials 2010 Capital Goods
21 Machinery 04 Manufacturing 20 Industrials 2010 Capital Goods
22 Electrical Equipment 04 Manufacturing 20 Industrials 2010 Capital Goods
23 Automobiles & Truck 02 Consumer Durable 25 Consumer Discretionary 2510 Automobiles & Components
24 Aircraft 04 Manufacturing 20 Industrials 2010 Capital Goods
25 Shipbuilding &  Railroad 04 Manufacturing 20 Industrials 2010 Capital Goods
26 Defence 04 Manufacturing 20 Industrials 2010 Capital Goods
27 Precious Metals 05 Energy 15 Materials 1510 Materials
28 Mining 05 Energy 15 Materials 1510 Materials
29 Coal 05 Energy 10 Energy 1010 Energy
30 Petroleum & Natural 05 Energy 10 Energy 1010 Energy
31 Utilities 06 Utilites 55 Utilities 5510 Utilities
32 Communication 07 Telecom 50 Telecommunication Services 5010 Telecommunication Services
33 Personal Services 01 Consumer Non-Durable 25 Consumer Discretionary 2530 Consumer Services
34 Business Services 08 Business Equipment & Services 20 Industrials 2020 Commercial Services & Supplies
35 Computers 08 Business Equipment & Services 45 Information Technology 4520 Technology Hardware & Equipment
36 Computer Software 08 Business Equipment & Services 45 Information Technology 4510 Software & Services
37 Electronic Equipment 08 Business Equipment & Services 45 Information Technology 4530 Semiconductors & Equipment
38 Measuring & Control 08 Business Equipment & Services 45 Information Technology 4520 Technology Hardware & Equipment
39 Business Supplies 04 Manufacturing 20 Industrials 2020 Commercial Services & Supplies
40 Shipping Containers 04 Manufacturing 20 Industrials 2030 Transportation
41 Transportation 04 Manufacturing 20 Industrials 2030 Transportation
42 Wholesale 09 Wholesale & Retail 25 Consumer Discretionary 2550 Retailing
43 Retail 09 Wholesale & Retail 25 Consumer Discretionary 2550 Retailing
44 Restaurants & Hotels 09 Wholesale & Retail 25 Consumer Discretionary 2530 Consumer Services
45 Banking 10 Finance 40 Financials 4010 Banks
46 Insurance 10 Finance 40 Financials 4030 Insurance
47 Real Estate 10 Finance 40 Financials 4040 Real Estate
48 Trading 10 Finance 40 Financials 4020 Diversified Financials
49 Miscellaneous 04 Manufacturing 99 Miscellaneous 9999 Miscellaneous
Fama & French 49 Industries GICS 10 Sector Groups GICS 24 Industry Groups
 77 
Table 2.21 reports investor sentiment predictability of alternative sector and industry 
portfolio returns. Equation 2.2 runs a regression of excess industry returns on a constant, 
investor sentiment, and the market-risk premium. The table reports the a1 regression 
coefficients from Equation 2.2 for the indicated k-week lags. Bold indicates statistical 
significance of 10 percent or greater estimated with White (1980) standard errors. The 
bottom two rows of the table report the total number of statistically significant 
coefficients for all sector and industry groups. The results reported in Table 2.21 
compare directly with results previously reported for the Fama and French 49 industries 
in Table 2.4. Results for investor sentiment predictability of alternative industry returns 
remain fundamentally unaltered from the previous analysis. All sector and industry 
groups have significantly positive predictability at a one-week lag. Predictability at 
8-week and 13-week lags remains mostly negative. Longer-term predictability at 26 and 
52 weeks is almost non-existent. Analysis of alternative industry classifications 
continues to support the main results. 
  
 78 
Table 2.21 Investor sentiment predictability using alternative industries 
 
Notes: Table 2.21 reports the a1 slope coefficients estimated with Equation 2.2. The analysis runs a regression of 
excess sector/industry returns on a constant, the indicated sentiment measures at different k-week lags, and the 
market-risk premium. The table reports results for sector and industry groups formed by mapping the Fama and 
French 49 industries alternatively to the Kacperczyk, Sialm, and Zheng (2005), GICS sector and GICS industry 
classifications, as Table 2.20 details. Sentiment measures are from the American Association of Independent 
Investors (AAII), Investors Intelligence (II), and Baker and Wurgler (BW). Bold indicates 10 percent or greater 
statistical significance estimated with White (1980) standard errors. 
2.11 Conclusion 
This study examines the interaction between investor sentiment and industry 
performance. As no universally accepted measure exists, the study examines return 
predictability using three popular investor sentiment measures. These measures are from 
the American Association of Independent Investors, Investors Intelligence and Baker 
Industry AAII II BW AAII II BW AAII II BW AAII II BW AAII II BW
Kacperczyk, Sialm, Zheng (2005):
Consumer Staples 0.016 0.014 0.001 0.002 -0.002 -0.001 0.000 -0.003 -0.001 0.001 0.001 0.000 -0.001 0.000 0.000
Consumer Durables 0.020 0.019 0.002 0.003 -0.003 -0.001 -0.002 -0.003 -0.001 -0.002 -0.001 0.000 -0.005 -0.004 -0.001
Healthcare 0.020 0.016 0.002 0.002 -0.006 0.000 -0.007 -0.007 -0.001 -0.001 0.000 0.000 -0.010 -0.011 -0.001
Manufacturing 0.018 0.016 0.001 0.003 0.000 -0.001 0.001 -0.001 0.000 -0.002 0.001 0.000 -0.004 0.000 0.000
Energy 0.013 0.019 -0.001 0.014 0.014 0.000 0.008 0.012 0.000 -0.002 0.002 0.003 -0.004 0.001 0.002
Utilites 0.005 0.005 0.000 0.002 0.003 0.000 0.003 0.005 0.000 0.002 0.002 -0.001 0.000 0.004 0.000
Telecom 0.015 0.012 0.003 -0.006 -0.008 -0.001 -0.009 -0.012 0.001 0.000 0.000 0.002 -0.004 0.000 0.000
Business Equip & Services 0.021 0.012 0.003 -0.002 -0.007 0.000 -0.010 -0.011 0.000 -0.001 -0.002 0.001 -0.007 -0.006 -0.001
Wholesale & Retail 0.018 0.019 0.001 0.002 -0.001 -0.001 -0.001 -0.003 -0.001 -0.001 0.001 0.000 -0.002 -0.001 -0.001
Finance 0.013 0.012 0.001 0.001 -0.003 -0.001 -0.003 -0.003 -0.001 -0.002 -0.001 0.000 -0.004 -0.003 0.000
GICS Sector Classification:
Energy 0.015 0.017 -0.002 0.015 0.012 0.000 0.012 0.016 0.000 0.005 0.004 0.002 -0.001 0.003 0.001
Materials 0.015 0.017 0.001 0.008 0.008 0.000 0.003 0.005 -0.001 -0.005 0.002 0.001 -0.004 0.001 0.001
Industrials 0.018 0.015 0.001 0.003 -0.001 -0.001 0.000 -0.002 0.000 -0.001 0.000 0.000 -0.004 -0.001 0.000
Consumer Durables 0.019 0.019 0.002 0.002 -0.002 -0.001 -0.002 -0.004 -0.001 -0.001 0.000 0.000 -0.003 -0.002 -0.001
Consumer Staples 0.013 0.011 0.000 0.004 -0.001 -0.001 0.002 0.000 -0.001 0.003 0.002 0.000 0.001 0.000 0.000
Healthcare 0.020 0.016 0.002 0.002 -0.006 0.000 -0.007 -0.007 -0.001 -0.001 0.000 0.000 -0.010 -0.011 -0.001
Financials 0.013 0.012 0.001 0.001 -0.003 -0.001 -0.003 -0.003 -0.001 -0.002 -0.001 0.000 -0.004 -0.003 0.000
Information Technology 0.022 0.012 0.003 -0.003 -0.007 0.000 -0.011 -0.011 0.000 -0.001 -0.002 0.001 -0.007 -0.006 -0.001
Telecom 0.015 0.012 0.003 -0.006 -0.008 -0.001 -0.009 -0.012 0.001 0.000 0.000 0.002 -0.004 0.000 0.000
Utilities 0.005 0.005 0.000 0.002 0.003 0.000 0.003 0.005 0.000 0.002 0.002 -0.001 0.000 0.004 0.000
GICS Industry Classification:
Energy 0.015 0.017 -0.002 0.015 0.012 0.000 0.012 0.016 0.000 0.005 0.004 0.002 -0.001 0.003 0.001
Materials 0.015 0.017 0.001 0.008 0.008 0.000 0.003 0.005 -0.001 -0.005 0.002 0.001 -0.004 0.001 0.001
Capital Goods 0.020 0.017 0.001 0.005 0.001 0.000 0.001 0.000 0.000 -0.002 0.002 0.000 -0.005 0.000 0.000
Commercial Services 0.016 0.013 0.001 0.002 -0.003 0.000 -0.002 -0.006 0.000 0.000 -0.001 0.000 -0.003 -0.002 0.000
Transportation 0.016 0.011 0.001 -0.002 -0.006 -0.002 0.000 -0.005 -0.001 -0.002 -0.003 0.000 -0.002 -0.001 0.000
Automobiles & Components 0.020 0.018 0.002 0.002 -0.004 -0.001 0.000 -0.003 -0.001 -0.001 0.001 0.000 -0.004 -0.002 0.000
Consumer Durables & Apparel 0.020 0.020 0.002 0.002 -0.003 -0.001 -0.002 -0.005 -0.001 -0.003 -0.002 0.000 -0.004 -0.003 -0.001
Consumer Services 0.019 0.019 0.001 0.001 -0.001 -0.001 -0.003 -0.004 -0.001 -0.002 0.001 0.000 -0.003 -0.003 -0.001
Media 0.018 0.016 0.002 0.002 -0.003 -0.001 0.000 -0.004 0.000 0.003 0.001 0.000 0.000 0.001 -0.001
Retailing 0.020 0.020 0.001 0.002 -0.002 -0.001 -0.002 -0.002 -0.001 -0.001 0.000 0.000 -0.003 -0.002 -0.001
Food & Staples Retailing 0.010 0.011 0.001 0.002 -0.002 -0.001 0.000 -0.003 0.000 0.001 0.002 0.000 -0.003 -0.002 0.000
Food, Beverages, & Tobacco 0.013 0.011 0.000 0.004 -0.001 -0.001 0.002 0.000 -0.001 0.004 0.002 0.000 0.002 0.000 0.000
Healthcare 0.018 0.015 0.002 0.002 -0.007 -0.001 -0.005 -0.010 -0.001 -0.001 -0.002 0.000 -0.008 -0.009 -0.001
Pharmaceuticals 0.024 0.018 0.002 0.002 -0.003 0.000 -0.010 -0.003 -0.001 -0.001 0.003 0.001 -0.015 -0.016 -0.002
Banks 0.009 0.009 0.000 0.000 -0.004 -0.001 -0.003 -0.004 -0.001 -0.002 -0.003 0.000 -0.005 -0.007 0.000
Diversified Financials 0.012 0.011 0.001 -0.002 -0.001 0.000 -0.005 -0.004 -0.001 -0.005 -0.003 0.001 -0.005 -0.001 0.000
Insurance 0.010 0.010 0.000 0.002 -0.001 -0.001 -0.003 -0.003 -0.001 -0.001 -0.002 -0.001 -0.001 -0.003 -0.001
Real Estate 0.020 0.019 0.002 0.003 -0.003 -0.002 -0.003 -0.002 0.000 -0.002 0.002 0.000 -0.005 -0.003 0.000
Software & Services 0.021 0.012 0.004 -0.006 -0.010 0.000 -0.016 -0.012 -0.001 0.001 0.001 0.001 -0.007 -0.006 -0.002
Technology Hardware & Equipment 0.022 0.010 0.003 -0.002 -0.009 0.000 -0.009 -0.013 0.000 -0.001 -0.004 0.001 -0.008 -0.008 -0.001
Semiconductors & Equipment 0.022 0.014 0.003 -0.001 -0.001 0.000 -0.009 -0.007 0.001 -0.002 0.000 0.002 -0.006 -0.003 0.000
Telecommunication Services 0.015 0.012 0.003 -0.006 -0.008 -0.001 -0.009 -0.012 0.001 0.000 0.000 0.002 -0.004 0.000 0.000
Utilities 0.005 0.005 0.000 0.002 0.003 0.000 0.003 0.005 0.000 0.002 0.002 -0.001 0.000 0.004 0.000
Other 0.021 0.019 0.002 0.004 -0.001 0.000 0.001 0.000 -0.001 0.002 0.002 0.001 -0.004 -0.004 0.000
Total Significant Positive 44 44 30 6 5 0 3 6 0 0 0 6 0 3 0
Total Significant Negative 0 0 2 3 12 16 14 15 11 1 0 0 16 6 3
1 week 8 week 13 week 26 week 52 week
 79 
and Wurgler (2006). First, the results confirm that investor sentiment positively predicts 
short-term and negatively predicts long-term market returns. The results also confirm 
that equal-weighted indices, in which small-cap stocks have a greater weight, are more 
susceptible to investor sentiment. Next, the results document widespread investor 
sentiment predictability of industry performance. At a one-week horizon, investor 
sentiment positively predicts performance for most industries. As expected, at long 
horizons, investor sentiment predicts negative industry performance. Interestingly, 
predictable performance differs between bull and bear markets, with greater 
predictability of reversals during bear markets. Additionally, in contrast to previous 
market studies, there is no evidence that industry-wide characteristics, which act as a 
proxy for valuation uncertainty, attract investor sentiment. Lastly, the study evaluates 
the practical application of an industry rotation strategy using investor sentiment. The 
strategy holds self-financing portfolios that are long (short) industries with the least 
(greatest) exposure to time-variant investor sentiment. Strategy performance ranges 
from 3 to 6 percent for different holding periods, which turnover and transaction costs 
would quickly dissipate. 
Overall, the study provides limited evidence of cross-sectional differences in investor 
sentiment predictability of industry performance and questions the practical application 
of investor sentiment in industry allocations. Better understanding how investor 
sentiment drives industry performance would require industry-specific measures of 
investor sentiment. For now, this remains a topic for further research. 
 80 
Chapter 3  Political Cycles in U.S. Industry Returns 
3.1 Introduction 
The popular press regularly features articles on the outlook for financial markets prior to 
U.S. presidential elections. A common perception is that stocks perform better under 
Republican presidents. In a seminal study, Hibbs (1977) states, “the Republican Party is 
generally viewed as being responsive to the interests of capital or business,” while “the 
Democratic Party has relatively close connections to organized labor and lower-income 
[groups].” Characteristic of that view, financial analysts regarded the 2004 Republican 
presidential candidate as “very pro-investment” and the Democratic candidate as 
negative for “market psychology.”39 Investor response to election news adds support to 
the popular belief that stocks perform best under Republican mandates. A study by 
Snowberg, Wolfers, and Zitzewitz (2008) provides evidence that “Republicans have 
consistently been the party of capital.” Analyzing presidential elections from 1880–
2004, they find that the stock market increases in value 2 percent more with news of a 
Republican victory than following news of a Democratic victory. 
However, empirical studies of political cycles in the U.S. stock market observe two 
important stylized facts. First, in presidential cycles,40 the stock market performs better 
under Democratic presidents. Empirical research, since Herbst and Slinkman (1984) and 
most recently Santa-Clara and Valkanov (2003), documents systematic stock market 
underperformance for Republican presidents. Second, in quadrennial cycles,41 the stock 
market performs better in the latter half of a four-year presidential term, irrespective of 
a president’s political affiliation. Politicians seeking reelection seemingly manipulate 
the economy to bolster investor support before elections. Since Nordhaus (1975) and 
Hibbs (1977), empirical research has documented four-year stock market cycles 
centered on presidential elections.42 
                                                 
39 http://www.businessweek.com/bwdaily/dnflash/jul2004/nf2004079_2333_db035.htm 
http://money.cnn.com/2004/01/29/markets/election_kerrystocks/ 
40 Also referred to in the literature as partisan cycles 
41 Also referred to in the literature as opportunistic cycles 
42 The “Stock Trader’s Almanac,” published by Yale Hirsch, originally popularized the “Presidential Cycle” trading 
strategy. http://www.investopedia.com/articles/financial-theory/08/presidential-election-cycle.asp 
 81 
While the literature extensively documents political cycles, systematic patterns in 
financial assets pose a challenge to market efficiency. Santa-Clara and Valkanov (2003) 
thoroughly investigate whether political cycles in the general stock market serve as a 
proxy for business cycles, but find the two cycles unrelated. Additionally, Santa-Clara 
and Valkanov (2003) examine whether expected returns explain political stock market 
cycles, concluding that higher returns during Democratic administrations systematically 
surprise investors. Cooper, McConnell, and Ovtchinnikov (2006) investigate the 
relationship between the January effect and political stock market cycles, finding no 
relationship between the two anomalies. Political stock market cycles, and the 
mechanism through which politics affects stock returns, remain an unresolved puzzle. 
This study investigates industry returns for evidence of political cycles, which 
potentially will explain documented evidence of political stock market cycles. As Santa-
Clara and Valkanov (2003) observe, political stock market cycles can comprise both 
unexpected and expected elements. Initially, the analysis examines realized industry 
returns for evidence of presidential and quadrennial cycles. The data covers 21 
presidencies from 1926–2006. Unexpected stock market performance can occur over 
time when Democratic or Republican policies positively or negatively surprise investors 
(Santa-Clara and Valkanov (2003)). For instance, a positive surprise would result if 
investors anticipate Democratic investment taxes that ultimately fail to materialize. 
Dominant industries, with cash flow sensitivity to policy surprises, in turn could 
potentially explain political cycles, which previous studies attribute to the general stock 
market. The analysis also includes an event study, to provide insight into whether the 
election of a Democratic or Republican president causes differences in expected 
industry performance. In efficient markets, stock prices should immediately reflect 
expected returns. Therefore, if investors anticipate differential industry performance 
between Democratic and Republican presidents, stock prices will reflect that difference 
upon election resolution. Differences in expected returns can arise due to a political risk 
premium, changes in expected [industry] cash flows, or both (Santa-Clara and Valkanov 
(2003)). As an illustration, if defense industry cash flows systematically increase under 
Republicans, then defense stock prices should increase with the election of a Republican 
president. Alternatively, differences in expected returns might merely reflect 
unsubstantiated beliefs on politically driven industry performance. To measure how 
 82 
political cycles affect expected industry returns, the analysis examines industry 
performance surrounding the closely contested and politically divergent 2004 Bush-
Kerry election. 
The results document the absence of political cycles in industry returns. The results 
confirm prior empirical studies, which document that the stock market performs better 
for Democrats and in the latter half of any presidency. In line with Santa-Clara and 
Valkanov (2003), stock market returns are 8.6 percent higher under Democratic 
presidents. Stock market returns are 10 percent higher in the second half of any 
presidency. Similar to the market, unadjusted industry returns are also higher under 
Democrats and the second half of any presidency. However, after correcting industry 
returns for risk with a Fama and French three-factor model, industry returns do not 
exhibit statistically significant returns over presidential or quadrennial cycles. There is 
also no evidence of significant differences in expected industry performance over the 
days immediately surrounding the 2004 election of a Republican president. The event- 
study analysis groups industries by political orientation based on past performance, 
political campaign contributions, and analyst recommendations. No matter the grouping, 
there is only scant evidence of statistically significant differences in expected industry 
returns following the 2004 election. Industries that have previously performed better 
under Republican presidents generate the highest excess returns – but only 
insignificantly so. The overall lack of statistically significant excess returns surrounding 
the election indicates that investors do not take political outcomes as a signal of 
expected industry performance. 
The results question the popular belief that political cycles drive industry 
performance and provide evidence that political cycles are solely market-wide 
phenomena, best explained at the macroeconomic level. Systematic differences in 
macroeconomic policies between Democrats and Republicans may provide an answer to 
the puzzling feature of political business cycles in the data. 
 83 
3.2 Literature Review and Hypotheses Development 
The literature documents two political stock market cycles: presidential cycles and 
quadrennial cycles.43 Presidential cycles depend on a president’s political party. 
Democratic and Republican Party social and economic agendas determine presidential 
cycles. Figure 3.1 illustrates average market returns for the past 21 presidential 
administrations. Stock market returns are above average for 80 percent of Democratic 
presidencies. In contrast, stock market returns are below average for 65 percent of 
Republican presidencies. A Republican was president during the 1929 and 1987 stock 
market crashes. The 1969–1974 bear market also occurred under a Republican 
president. Conversely, a Democrat was president during the 1960s and 1990s bull 
markets (Gross (2004)). Niederhoffer, Gibbs, and Bullock (1970) provide evidence of 
systematic and statistically significant Democratic outperformance. 
An influential study by Santa-Clara and Valkanov (2003) investigates election cycles 
in the U.S. stock market across 18 presidential elections from 1927 to 1998. The  central 
finding of Santa-Clara and Valkanov (2003) is that the stock market performs 
systematically better during Democratic presidencies. As a possible explanation, Santa-
Clara and Valkanov (2003, pg. 1842) investigate whether the difference in market 
outperformance is due to a “Democratic risk premium.” Higher market volatility during 
Democratic presidencies would indicate that excess returns were merely rational 
compensation for risk. However, Santa-Clara and Valkanov (2003) observe that stock 
market volatility actually decreases under Democrats. Santa-Clara and Valkanov (2003) 
confirm that presidential cycles are independent of normal business cycles. Lastly, 
Santa-Clara and Valkanov (2003) examine the response of investors to election 
outcomes, documenting no difference in expected returns with the election of a 
Democratic president. Santa-Clara and Valkanov (2003, pg. 1870) conclude, “The small 
market reaction to election news is difficult to reconcile in a rational-expectations 
framework.” 
Quadrennial cycles depend on the year of a four-year presidential term. Self-serving 
political agendas determine quadrennial cycles. Booth and Booth (2003) and Gärtner 
                                                 
43 Alesina, Roubini, and Cohen (1997) and Drazen (2000) provide good surveys of political stock market cycles. 
 84 
and Wellershoff (1995) document higher stock market returns in the latter half of any 
presidential term. Nordhaus (1975) and Alesina (1989) conjecture that politicians, 
motivated by reelection desires, generate political cycles through manipulative 
economic policies. For instance, politicians seeking reelection lower interest rates to 
increase employment. After the election, they then increase interest rates to restore the 
economy to sustainable growth levels. Ideologically, investors view Democrats as 
favoring low unemployment at the expense of higher inflation and Republicans as doing 
the opposite (Hibbs (1977)). A number of studies provide empirical evidence of 
quadrennial cycles. Allvine and O'Neil (1980) use Standard & Poor’s 400 data from 
1948 to 1978. Their study documents stronger evidence of quadrennial cycles in the 
latter half of their sample, attributed to more adroit political macroeconomic 
manipulation. A later study by Beyer, Jensen, and Johnson (2008) confirms quadrennial 
cycles in small-cap and large-cap market indices with data from 1957 to 2004. Most 
recently, Beyer, Jensen, and Johnson (2008) show that quadrennial cycles are most 
evident in small-cap stocks and during the third year of a presidency. 
Figure 3.1 Value-weighted market returns by U.S. presidential administration 
 
Notes: Figure 3.1 depicts market performance reported for presidential administrations over the sample period from 
July 1927 to June 2006. 
 85 
The mechanism through which political cycles act on stock market returns remains 
an open question.44 Investors systematically appear to ignore the empirical fact that 
stocks perform better under Democrats. Santa-Clara and Valkanov (2003) find no 
significant difference in expected market returns resulting from presidential elections. 
Additionally, they find no evidence that political stock cycles reflect differences in 
expected returns related to business cycle fluctuations. Rather, Santa-Clara and 
Valkanov (2003) observe that unexpected returns fully explain the return differential 
between Democratic and Republican administrations. Unexpected returns can result 
from political agendas that surprise the market or from investor inattention to previous 
political stock cycles. A logical question one could then ask is why investors fail to 
anticipate the impact of presidential elections on the stock market. A political risk 
premium could potentially account for systematically higher market performance under 
Democrats. However, counter to that argument, Leblang and Mukherjee (2004) and 
Santa-Clara and Valkanov (2003) actually document lower stock market volatility when 
a Democrat holds office. In the absence of a risk premium explanation, systematically 
higher market returns under Democrats leave an unanswered puzzle and a gap in the 
literature. This study attempts to fill that gap, by investigating whether industry 
performance provides any additional insight into political stock cycles. 
Industry cash flows can provide a direct mechanism for political cycles to act on 
stock returns. Certain industries, with cash flow sensitivity to political agendas, can 
potentially drive political stock market cycles. Democrats and Republicans, for instance, 
have distinctive agendas for health care and national defense. Political agendas, thus, 
can significantly affect the profitability of those highly capitalized industries. As an 
example, defense spending increased by 40 percent under a Republican president from 
1981–1989.45 Similarly, quadrennial cycles potentially affect the cash flows of 
industries most sensitive to interest rate adjustments, such as the finance and 
construction industries. Herron, Lavin, Cram, and Silver (1999), observing the 1992 
election, find investors capitalize election probabilities differently across industries. 
Defense, aerospace, finance, and pharmaceuticals were the most politically sensitive 
                                                 
44 See the conclusions of Santa-Clara and Valkanov (2003) for a complete discussion. 
45 http://en.wikipedia.org/wiki/Ronald_Reagan 
 86 
industries at that time. Industry returns around key election events reflect changes in 
expected returns. For example, financial analysts considered a 2004 Republican victory 
positive for energy, utilities, and pharmaceuticals.46 Those industries surged in value an 
average 4.5 percent following the Republican convention and renomination of George 
W. Bush.47 Industry cash flow sensitivity to political agendas suggests a role for 
industries in describing political stock market cycles. 
The above discussion leads to a formal statement of this study’s null and alternative 
hypotheses.  
H0: There is no relationship between presidential election cycles and industry returns. 
H1: The political affiliation of a president determines industry returns. 
H2: The year of a presidential term in office determines industry returns. 
H3: Presidential elections determine investor expectations of future industry returns. 
3.3 Results for the General Market 
The principal purpose of this section is to replicate and verify prior research, such as 
Santa-Clara and Valkanov (2003), documenting political cycles in US stock market 
indices. Santa-Clara and Valkanov (2003) document three central findings: market 
returns are higher under Democrats; there is a small capitalization stock premium under 
Democratic presidents; and market returns are higher the second half of any presidential 
term. 
If stock markets follow a random walk, information on whether a president is a 
Democrat or Republican or the year of a presidential term should have no effect on 
expected returns. Therefore, empirical studies, such as Santa-Clara and Valkanov 
(2003), that document a correlation between political election cycles and general stock 
market returns contradict financial theory. Consider, for instance, the simple regression 
equation 
                                                 
46 As a further illustration, the largest Swiss bank, Banque Vontobel, introduced two mutual funds in 2000. The Bush 
fund held stocks in industries expected to benefit under Republicans: Philip Morris, Pfizer, Microsoft, General 
Dynamics, Lockheed Martin and International Paper. The Gore fund held stock in industries expected to benefit 
under Democrats: Merck, Fannie Mae, Freddie Mac, Devry Inc, Ballard and United Technologies. 
47 CNN Money provides a detailed account of market expectations for a Republican victory. 
http://money.cnn.com/2004/08/30/markets/election_stocks/index.htm\ 
 87 
0 1t t tR RPD                    (Eq. 3.1) 
where excess market returns (R) are regressed on a constant and a presidential cycle 
dummy variable (RPD), with the usual white-noise error term () controlled for 
heteroskedasticity and autocorrelation following Newey and West (1987). The 
presidential cycle dummy variable (RPD) takes the value of one under a Republican 
president and zero otherwise. The intercept 0 represents average returns under 
Democrats and the 1 coefficient represents the marginal difference in returns between 
Republicans and Democrats. If the simple regression equation holds, whether a 
president is a Republican or Democrat should not have a systematic effect on stock 
returns and 1 should equal zero. In efficient markets, returns should follow a random 
walk. Consequently, political variables should contain no explanatory power. Similarly, 
consider the regression equation 
0 1 2t t tR H D                    (Eq. 3.2) 
where excess market returns (R) are regressed on a constant and the quadrennial cycle 
dummy variable (H2D) with the same Newey and West (1987) adjusted standard errors 
as above. The quadrennial cycle dummy variable (H2D) takes the value one during the 
second half of any presidential administration and zero otherwise. Here, the intercept 0 
represents average first-half returns and the 1 coefficient represents the marginal 
difference in returns between the first and second half of a four-year U.S. presidential 
term. Again, in the absence of a quadrennial cycle, the year of a presidential term should 
not have a predictable effect on stock returns and the 1 coefficient should equal zero. 
Nevertheless, the empirical financial literature documents both presidential and 
quadrennial cycles in the general stock market. 
The analysis of political business cycles in the general stock market covers 80 years 
or 21 presidential administrations, spanning 1926–2006. Panel A of Table 3.1 reports 
results from Equation 3.1 for the value-weighted general market index, Fama-French 
size and value factors, and short-term interest rates. The one-month Treasury bill from 
Ibbotson Associates proxies the risk free rate. The size factor SMB (small minus big) 
and value factor HML (high minus low) are well-known risk factors described by Fama 
 88 
and French (1993). The value-weighted market and industry returns, the one-month 
Treasury bill rate, size factor, and value factor come from the Kenneth French website.48 
Table 3.1 Market descriptive statistics for presidential and quadrennial cycles 
 
Notes: Panel A of Table 3.1 reports annualized means, standard deviations, and presidential cycle results for excess 
value-weighted market returns, size factor (SMB), value factor (HML), and the one-month Treasury bill.49 The sample 
period covers 1926–2006. Equation 3.1 estimates average excess returns for Republican (REP) or Democratic (DEM) 
presidencies, with a regression of excess returns (R) on a constant and the Republican dummy variable (RPD). The 
presidential cycle dummy variable (RPD) takes the value of one under a Republican president and zero otherwise. 
The 0 intercept measures Democratic (DEM) performance, 1 measures marginal Republican performance (DIF), 
and (0 + 1) measures Republican (REP) performance. Panel B reports annualized means, standard deviations, and quadrennial cycle results for excess value-weighted market returns, size factor (SMB), value factor (HML), and the 
one-month Treasury bill. The sample period covers 1926–2006. Equation 3.2 estimates average returns excess for the 
first half (HLF1) or second half (HLF2) of a four-year U.S. presidential cycle, with a regression of excess returns (R) 
on a constant and the timing variable (H2D). The quadrennial cycle variable (H2D) takes the value of one during the 
second half of a four-year U.S. presidential cycle and zero otherwise. The intercept 0 measures average first-half 
performance, 1 measures the marginal difference (DIF) in returns between first-half and second-half performance, 
and (0 + 1) measures second-half (HLF2) performance. Panel A and Panel B indicate statistically significant differences (DIF) at 1%***, 5%**, and 10%* based on Newey and West (1987) heteroskedasticity and 
autocorrelation corrected test-statistics. 
For the entire period, excess market returns over the one-month Treasury-bill rate 
average 6.2 percent per annum. However, average excess returns of 10.6 percent for 
Democrats are much higher than average excess returns of 1.9 percent for Republicans. 
The difference represents an economically large and statistically significant 
outperformance of 8.6 percent for Democrats. Similarly, Santa-Clara and Valkanov 
(2003) document a 9 percent difference in returns between Democrats and Republicans 
using monthly returns for the AMEX, NYSE, and NASDAQ indexes from 1926 to 
1998. Swensen and Patel (2004) observe annual returns from 1969-2000 for the NYSE 
composite and industrial, transportation, utility, and financial sub-indexes. Swensen and 
                                                 
48 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
49 See Fama and French (1993) for a discussion of the (SMB) size and (HML) value factors. 
Panel A: Presidential Cycle
Description Mean  Std.Dev. REP DEM DIF
Excess value-weighted market return 6.2% 18.9% 1.9% 10.6% -8.6% **
Size factor (SMB) 2.2% 11.3% -0.8% 5.4% -6.2% **
Value factor (HML) 4.3% 12.1% 4.5% 4.1% 0.3%
Treaury bill 3.7% 0.9% 4.7% 2.7% 1.9% ***
Panel B: Quadrennial Cycle
Description Mean  Std.Dev. HLF1 HLF2 DIF
Excess value-weighted market return 6.2% 18.9% 1.3% 11.3% -10.0% **
Size factor (SMB) 2.2% 11.3% -0.1% 4.7% -4.8% *
Value factor (HML) 4.3% 12.1% 4.8% 3.8% 0.9%
Treaury bill 3.7% 0.9% 3.9% 3.5% 0.3%
 89 
Patel (2004) find average returns to the NYSE composite are 5 percent greater under 
Democratic administrations. Likewise, Swensen and Patel (2004) show returns for 
industrials (5.3%), financials (7.0%), transportation (7.2%), and utilities (10.3%) sub-
indices are uniformly higher for Democratic administrations. 
The results confirm that small-cap firms have higher returns under Democrats, as 
previous studies also show. As indicated by the SMB factor, excess returns to small-cap 
firms earn a 5.4 percent premium under Democrats compared with a 0.8 percent 
discount under Republicans. The 6.2 percent difference is economically and statistically 
significant. Likewise, Hensel and Ziemba (1995) find higher returns for small-cap 
NYSE stocks under Democratic presidents for the years 1928 to 1993. Similarly, Santa-
Clara and Valkanov (2003) observe the difference in returns to size-decile portfolios 
between Republican and Democratic administrations. Santa-Clara and Valkanov (2003) 
show that, while all decile portfolios have higher returns under Democrats, relative 
outperformance increases monotonically from large to small capitalization. While one 
can argue that small-cap firms are typically more risky than large-cap firms are, any 
capitalization premium should be independent of a president’s political affiliation. 
However, Santa-Clara and Valkanov (2003) show presidential cycle effects are most 
pronounced in small-cap firms. 
The results show no indication of a glamour stock effect between Republicans and 
Democrats in the general stock market. A glamour effect would be evident if high-
growth firms, with large market-value-to-book-value ratios, performed better under a 
given political party. However, a look at the HML value factor shows similar returns 
across administrations for Republicans (4.5%) and Democrats (4.1%). 
A look at Treasury bill rates reveals slightly higher interest rates under Republican 
administrations. The observation comes as no surprise given Republican administrations 
typically pursue tighter monetary policy. Over the last 80 years, short-term interest rates 
average 4.7 percent under Republicans and 2.7 percent under Democrats, for an 
approximate 200-basis-point difference. A study by Johnson and Chittenden (1999), 
which covers the 1929–1996 period, likewise documents a 2.7 percent difference in 
interest rates favoring Republicans. Considering average market volatility of 
approximately 19 percent over the past eight decades, it seems unlikely, however, that 
 90 
such a relatively small difference in interest rates between political parties would 
influence stock returns. A study by Durham (2005) also concludes that the impact of 
“surprises” in monetary policy on stock returns is minimal compared with overall equity 
return volatility. Nonetheless, regardless of any marginal difference in interest rate 
levels between administrations, investors would have been better off investing in the 
general market rather than short-term Treasury bills under either Democrats or 
Republicans. 
Additionally, the analysis investigates the general stock market index for evidence of 
quadrennial cycles. Systematically different returns over presidential terms could 
indicate that incumbent presidents, irrespective of political party, manipulate the 
economy to improve chances for reelection. Panel B of Table 3.1 reports results from 
Equation 3.2 for the general market index, the Fama-French factors, and interest rates. 
While excess market returns average 6.2 percent from 1926 to 2006, most appreciation 
occurs during the second half of a presidential term. The results show an economically 
and statistically significant 10 percent difference in excess returns between the first 
(1.3%) and second half (11.3%) of a presidential term. 
The results confirm the work of previous studies that document quadrennial cycles in 
stock returns. Research by Allvine and O'Neil (1980), Huang (1985), Johnson and 
Chittenden (1999), and Booth and Booth (2003), among others, find four-year asset 
return cycles coinciding with presidential administrations for the general market. 
Swensen and Patel (2004) observe quadrennial cycles in the NYSE composite index, as 
do Hensel and Ziemba (1995) in large-cap and small-cap stocks. 
While most previous research finds evidence of a quadrennial cycle in asset returns, 
there are exceptions. In his study, Banning (2002) finds no statistically significant 
difference between first- and second-half returns using daily data for the Dow Jones 
Industrial Average covering the 1897–2000 period. The choice of short-frequency data 
perhaps explains the inconsistency with other studies, which typically use less noisy 
longer horizon monthly or quarterly data. Interestingly, the Banning (2002) study finds 
a statistically significant difference in returns during a president’s first complete term in 
office compared with subsequent terms. 
 91 
As with the investigation of presidential cycles, this study also considers the 
correlation between quadrennial cycle and market cap, market values, and Treasury bill 
rates. The SMB size-factor confirms a statistically significant 4.8 percent higher second-
half return to small stocks. However, as with presidential cycles, no value effect is 
evident in quadrennial cycles. Treasury bill rates are constant across presidential terms, 
at 3.9 percent and 3.5 percent for the first and second half. 
To summarize, the empirical results to this point confirm a systematic relationship 
between market performance and both a president’s political affiliation and the year of a 
president’s term. Notably, contrary to popular belief, general stock market indices 
perform better under a Democratic president. A political effect is even more evident 
with small-cap stocks than large-cap stocks. As previous studies also show, interest 
rates are higher with a Republican in the White House. Additionally, stock market 
returns are higher during the second half of a four-year presidential term, regardless of 
political affiliation. 
While the literature documents presidential and quadrennial cycles, financial theory 
fails adequately to explain the existence of these political cycles. Studies put forth a 
number of different theories as explanations. One possible explanation is that political 
cycles may simply track business cycles. To correct for this possibility, Santa-Clara and 
Valkanov (2003) include well-known business-cycle variables in their model. 
Surprisingly, even with the addition of dividend yield, term-spread, default-spread, and 
relative interest rate variables, Santa-Clara and Valkanov (2003) find results for 
presidential cycles are more robust. They conclude that political cycles are unrelated to 
recurring business cycles. 
Another argument, in a risk and return paradigm, is that higher returns under 
Democratic administrations are simply compensation for additional risk, as measured by 
increased stock volatility. However, in contrast to the expected higher volatility under 
Democrats required to substantiate this argument, Santa-Clara and Valkanov (2003) 
actually observe higher variance in stock returns under Republicans. They argue higher 
volatility possibly results from increased market liquidity under Republicans, given 
investor expectations of higher returns. A study of the 2000 U.S. presidential election 
 92 
(Leblang and Mukherjee (2004)) concludes markets are less volatile when it appears a 
Democrat will become president. 
Campbell and Li (2004) observe differences in volatility as an explanation for the 
presidential premium. While typically political cycle studies use OLS regressions with 
HAC adjusted standard errors, Campbell and Li (2004) question the validity and 
efficiency of OLS parameter estimates in calculating presidential cycle premiums. 
Alternatively, Campbell and Li (2004) use a variety of methods, such as weighted least 
squares (WLS) and GARCH models, to account for time variant market volatility. 
Generally, they conclude that the differences in returns to large stocks between 
Republicans and Democrats, although still persistent, are smaller than OLS estimates 
and lack statistical significance. However, even with different methodologies, a small-
cap stock premium of 6.1 percent to 11.9 percent, depending respectively on GARCH or 
WLS estimates, remains relatively large and statistically significant under Democratic 
administrations. 
Other studies consider whether differences in excess returns between political parties 
represent conditional differences in investor expectations. Riley and Luksetich (1980) 
use Standard & Poor’s 500 return data from 1900 to 1976 to conduct an event study 
surrounding key election days. Riley and Luksetich (1980) observe that although the 
market responds favorably in the short term to a Republican victory, investors show no 
long-term political preference except possibly for an incumbent. Similarly, Santa-Clara 
and Valkanov (2003) look at market reaction in the days surrounding presidential 
elections from 1926 to 1998. They conclude the market does not price election 
outcomes. Differences in investor expectations, given the party in power, inadequately 
explain the systematically large and seemingly unexpected general stock market returns 
that favor Democratic administrations. 
The literature provides few alternative explanations of what might cause documented 
quadrennial cycles. Allvine and O'Neil (1980) argue that partially efficient markets 
explain the persistence of four-year cycles. They suggest short-sale restrictions faced by 
institutional investors limit the ability to exploit downside opportunities during the first 
half of an administration. Moreover, they argue that investor limitations in processing 
copious amounts of political information distort otherwise efficient markets. Swensen 
 93 
and Patel (2004) look at inflation rates and required real rates of returns. While inflation 
rates are higher in the last two years of a presidency, particularly during Republican 
administrations, they find real returns remain larger and statistically significant. Lastly, 
Swensen and Patel (2004) suggest quadrennial cycles may partially be explained by 
control of the Congress. However, the presence of quadrennial cycles, like presidential 
cycles, in the data remains largely unexplained. 
The above analysis confirms the presence of political stocks previously documented 
in the literature. In the sections that follow, the analysis investigates industry returns as 
a possible explanation for political cycles in the general stock market. If political effects 
are exceptionally strong in dominant industries, the observed market-wide phenomenon 
might be only industry specific. If political effects were industry specific, industry 
returns should remain significant after correcting for general market movements and 
should persist across sub-periods. Otherwise, general macroeconomic determinants 
might provide a more likely explanation. Therefore, the analysis first tests whether 
political and quadrennial cycles are present in industry returns after adjusting for 
general market movements. The initial test simply uses a single-index model. Moreover, 
as other studies observe that differences in returns under Democrats and Republicans 
are particularly large for smaller firms, additional analysis adjusts industry 
outperformance with the Fama-French three-factor model. 
3.4 Industry Presidential Cycles 
The industry data covers the same 1926 to 2006 period, spanning eight decades and 21 
presidential administrations. The 48 industry portfolios represent all stocks included in 
the NYSE, AMEX, and NASDAQ indices grouped by standard industrial 
classifications, as the Kenneth French website describes in detail.50 
Table 3.2 reports the basic characteristics of all 48 industries for returns in excess of 
the Treasury bill rate reported as annualized returns. The highest unconditional return is 
banking (9.3%) followed by aircraft (8.8%), beer & liquor (8.3%) and tobacco (8.3%). 
It is interesting to observe that within a risk and return paradigm, tobacco has the third-
                                                 
50 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_48_ind_port.html 
 94 
highest return of all industries, yet it has the eighth smallest return variance and the 
smallest systematic risk. Conversely, real estate has the second-lowest return for all 
industries (0.6 percent) but a relatively large market beta. 
Table 3.2 Industry summary statistics for presidential and quadrennial cycles 
 
Notes: Table 3.2 reports average firm size (million USD), excess returns, standard deviation, and market beta for the 
full period. Equation 3.1 estimates industry performance for presidential cycles under Republican (REP) and 
Democratic (DEM) administrations. Equation 3.2 estimates industry performance for quadrennial cycles over the first 
half (HLF1) or second half (HLF2) of a four-year U.S. presidential term. The table indicates statistically significant 
differences (DIF) for presidential and quadrennial cycles at 1%***, 5%**, and 10%*, based on Newey and West 
(1987) heteroskedasticity and autocorrelation corrected test-statistics. 
Industry Size Mean  Std. Dev. Beta REP DEM DIF HLF1 HLF2 DIF
Agriculture 219 4.3% 25.9% 0.92 -2.3% 11.3% -13.6% ** 0.8% 7.9% 7.1%
Food Products 718 7.1% 17.0% 0.74 7.7% 6.6% 1.1% 4.1% 10.2% 6.1%
Beer & Liquor 2,388 8.3% 25.2% 0.97 4.0% 12.8% -8.8% 7.3% 9.3% 2.0%
Tobacco Products 3,695 8.3% 20.3% 0.63 10.7% 6.0% 4.7% 6.8% 9.9% 3.1%
Recreation 136 3.4% 33.5% 1.21 1.2% 5.7% -4.6% -6.4% 14.2% 20.5% **
Entertainment 347 6.6% 32.2% 1.39 0.9% 12.5% -11.6% 3.1% 10.1% 7.0%
Printing and Publishing 505 6.1% 26.2% 1.06 3.1% 9.1% -6.1% 4.3% 7.9% 3.6%
Consumer Goods 700 5.5% 19.6% 0.85 2.2% 8.9% -6.6% 2.0% 9.1% 7.1%
Apparel 162 6.6% 23.8% 0.99 2.1% 11.4% -9.3% 0.8% 12.8% 12.0% *
Medical Equipment 248 7.6% 22.1% 0.85 4.3% 11.0% -6.7% 6.7% 8.5% 1.8%
Pharmaceutical Products 879 7.9% 20.5% 0.85 5.5% 10.3% -4.8% 7.2% 8.6% 1.4%
Chemicals 723 6.8% 21.7% 1.02 4.1% 9.6% -5.5% 2.7% 11.1% 8.3%
Textiles 142 4.4% 26.1% 1.14 -0.5% 9.6% -10.1% 0.2% 8.8% 8.6%
Construction Materials 267 5.7% 23.3% 1.12 1.6% 10.0% -8.4% 1.4% 10.3% 8.9%
Construction 170 3.8% 32.8% 1.35 -5.8% 14.4% -20.3% *** -3.3% 11.4% 14.7% *
Steel Works Etc 311 4.3% 28.0% 1.29 -0.6% 9.5% -10.0% -2.0% 11.1% 13.1% **
Machinery 260 6.4% 24.9% 1.22 0.4% 12.7% -12.3% ** 0.3% 12.9% 12.6% **
Electrical Equipment 522 7.7% 26.5% 1.27 2.9% 12.7% -9.8% 2.0% 13.8% 11.8% *
Automobiles and Trucks 682 6.4% 26.7% 1.19 1.5% 11.5% -10.0% 0.0% 13.1% 13.2% *
Aircraft 1,121 8.8% 32.6% 1.32 3.2% 14.7% -11.5% -2.6% 21.4% 24.0% ***
Shipbuilding, Railroad Equipment 357 4.5% 27.2% 1.12 -2.2% 11.5% -13.6% ** -0.9% 10.0% 10.9%
Non-Metallic & Industrial Metal Mining 298 5.8% 23.4% 0.95 1.6% 10.2% -8.7% -1.5% 13.6% 15.2% ***
Coal 371 7.3% 29.4% 0.78 0.8% 14.1% -13.3% ** -0.3% 15.4% 15.6% **
Petroleum and Natural Gas 1,008 7.5% 21.0% 0.86 1.4% 13.8% -12.4% ** 1.7% 13.6% 11.9% **
Utilities 761 5.2% 19.8% 0.80 4.9% 5.6% -0.7% -0.1% 10.8% 10.9% **
Communication 1,772 5.4% 15.9% 0.64 5.3% 5.6% -0.3% 2.9% 8.0% 5.2%
Business Services 265 5.9% 26.5% 0.96 -1.2% 13.5% -14.7% ** 2.3% 9.6% 7.4%
Computers 769 8.1% 25.7% 1.10 2.3% 14.2% -11.8% * 3.3% 13.1% 9.7%
Electronic Equipment 376 6.0% 30.8% 1.37 -4.2% 17.2% -21.4% *** 0.2% 12.1% 11.9%
Measuring and Control Equipment 334 6.6% 24.6% 1.01 0.9% 12.7% -11.8% ** 2.2% 11.3% 9.1%
Shipping Containers 410 6.9% 21.3% 0.94 7.1% 6.7% 0.4% 5.1% 8.7% 3.6%
Transportation 380 4.6% 24.6% 1.12 -0.1% 9.5% -9.6% 0.4% 9.0% 8.6%
Wholesale 157 2.7% 26.4% 1.10 -6.2% 12.5% -18.7% *** -1.1% 6.7% 7.7%
Retail 550 6.6% 21.1% 0.96 4.1% 9.2% -5.1% 1.5% 11.9% 10.4% *
Restaurants, Hotels, Motels 221 6.8% 24.5% 1.00 3.3% 10.4% -7.2% 0.2% 13.8% 13.6% **
Banking 356 9.3% 24.6% 1.03 4.5% 14.4% -9.8% 4.6% 14.3% 9.6%
Insurance 691 6.3% 25.9% 1.10 1.1% 11.8% -10.8% ** 2.9% 9.9% 7.0%
Real Estate 78 0.6% 33.2% 1.25 -7.5% 9.4% -16.8% ** -7.0% 8.9% 15.9% *
Trading 479 6.6% 26.3% 1.26 1.2% 12.1% -10.9% * -2.3% 16.2% 18.5% ***
Other 797 1.7% 25.9% 1.06 -6.8% 11.0% -17.7% *** -4.2% 8.0% 12.2% *
Personal Services 147 3.2% 32.3% 1.12 -3.4% 10.2% -13.6% * -3.8% 10.7% 14.5% *
Rubber and Plastic Products 108 6.9% 27.0% 1.14 0.9% 12.6% -11.7% * 3.2% 10.8% 7.6%
Candy & Soda 692 7.6% 23.7% 0.88 7.2% 8.0% -0.9% 5.0% 10.4% 5.4%
Business Supplies 631 3.6% 43.0% 1.47 -5.5% 12.9% -18.4% * 0.9% 6.6% 5.7%
Healthcare 314 0.8% 36.1% 1.25 -8.2% 15.6% -23.8% * -15.3% 19.8% 35.0% ***
Fabricated Products 110 -1.0% 24.2% 1.10 -5.4% 5.7% -11.1% -11.9% 11.2% 23.0% ***
Defense 1,121 5.4% 23.6% 0.83 3.6% 8.2% -4.6% 0.1% 11.0% 10.9%
Precious Metals 435 2.9% 35.3% 0.68 0.6% 6.4% -5.9% -0.1% 6.0% 6.1%
 95 
0 1t t tR RPD                    (Eq. 3.1) 
Equation 3.1 runs a regression of excess returns for each of the 48 industry portfolios 
(R) on the presidential cycle dummy variable (RPD) previously described. 
Observing conditional excess industry returns for Republicans (REP) and Democrats 
(DEM), reported in Table 3.2, the results – not surprisingly – show a strong 
outperformance of most industries under Democrats. There are notable exceptions. The 
largest relative outperformance in returns between administrations is tobacco (4.7%) 
and food products (1.1%) under Republicans. In comparison, industries with the largest 
relative outperformance under Democrats are healthcare (35%) and aircraft (24%). 
Another strong performer for Democrats, the construction industry, typified by small 
contractors, shows a 20.3 percent difference (DIF) over Republicans. These initial 
observations support conventional wisdom, which holds that Republican policies 
support tobacco interests and big industries such as food processing and that Democratic 
policies support healthcare, technology, and small-scale industries.51 There are some 
surprises, however, that contradict popular perceptions. Perhaps most notably, defense 
shows a 4.6 percent outperformance when Democrats hold office. Likewise, natural 
resource extraction industries, such as coal and petroleum, show a strong performance 
under Democrats. Figure 3.2 illustrates industry returns sorted left to right from highest 
Republican outperformance to highest Democratic outperformance. 
  
                                                 
51 “Words from the Trading Floor,” CNN financial, Christine Romans, 01 September, 2004. 
 96 
 
Figure 3.2 Industry returns over presidential cycles 
 
Notes: Figure 3.2 illustrates excess industry returns over presidential cycles for Republican and Democratic 
presidencies estimated with Equation 3.1. The presidential cycle dummy variable (RPD) takes the value of one if a 
Republican is president and zero otherwise. The table sorts returns from left to right by the largest difference in 
returns between Republican administrations and Democratic administrations. The 0 intercept measures average 
returns under Democratic administrations and (0 + 1) measures average industry returns under Republican administrations. 
Market-wide political cycles may explain the industry results. To correct for this 
possibility, Equation 3.3 adjusts for industry returns with the inclusion of a term for 
relative market movements, using a modified single-index model. The model now 
becomes a market model with the inclusion of a political variable, which effectively 
divides alpha returns between Democratic and Republican administrations. 
0 1 1( - )t t mt t tR RPD r rf                     (Eq. 3.3) 
Table 3.3 reports the results from Equation 3.3. Relative to the market, there are six 
industries with positive outperformance and seven industries with negative 
outperformance for Republicans that are statistically significant at a level of 10 percent 
 97 
or greater. After a market correction, industries with large capitalization, such as 
tobacco, continue to provide Republicans the best performance. Small-scale industries, 
such as healthcare and construction, provide Republicans the least performance. 
Relative to the market, there is a substantial change in the number of excess industry 
returns that remain statistically significant for Democrats. The petroleum industry is 
now the sole industry with significantly positive returns for Democrats, while returns to 
the steel industry turn significantly negative. Statistically significant positive differences 
increase from virtually none to five industries for Republicans and decrease from 20 to 
four industries for Democrats. The largest difference in statistically significant returns 
remains tobacco (10.0%) for Republicans and wholesale (8.8%) and electronic 
equipment (8.8%) for Democrats. 
As a robustness check, the analysis divides the full time period in half to observe if 
results are persistent across sub-periods. Only one industry, food products, has a 
statistically significant difference in returns between Republican and Democratic 
administrations for all periods. Even tobacco, which had a highly significant 
outperformance for Republicans, is not statistically significant in the sub-periods. The 
results, to this point, indicate industry returns conditional on political control provide no 
relative outperformance for investors, once controlled for general market movements. 
  
 98 
Table 3.3 Industry presidential cycles with a market correction 
 
Notes: Table 3.3 reports regression results estimated with Equation 3.3 for industry outperformance over presidential 
cycles corrected for general market movement. The model equates to a single-index model with the inclusion of a 
political dummy variable (RPD) that takes the value of one if a Republican is president and zero otherwise. The 0 
intercept measures Democratic (DEM) outperformance, 1 measures marginal Republican outperformance (DIF), and 
(0 + 1) measures Republican (REP) outperformance. The table reports results for the full period and two equal sub-periods and indicates statistical significance at 1%***, 5%**, and 10%* based on Newey and West (1987) 
heteroskedasticity and autocorrelation corrected test-statistics. 
As a final exercise, the analysis investigates if sensitivities to industry size and value 
risk factors explain excess returns. With the inclusion of size (SMB) and value factors 
(HML), discussed in Fama and French (1993), the model becomes a modified Fama-
French three-factor model. Table 3.4 reports the estimation results from Equation 3.4. 
Industry REP DEM DIF REP DEM DIF REP DEM DIF
Agriculture -3.9% 1.5% -5.4% -6.7% 1.0% -7.6% -2.3% 2.5% -4.8%
Food Products 6.2%*** -1.2% 7.4%*** 4.3% * -0.8% 5.1% * 7.4% *** -1.9% 9.3% **
Beer & Liquor 2.2% 2.4% -0.2% -0.6% 3.2% -3.8% 4.0% 0.7% 3.3%
Tobacco Products 9.5%*** -0.6% 10.0% ** 7.7% * -0.7% 8.4% 10.5% *** -0.3% 10.8%
Recreation -1.0% -6.4% 5.4% -2.8% -2.8% 0.0% 0.1% -12.5% *** 12.6% **
Entertainment -1.6% -2.1% 0.5% -8.6% * -4.7% -3.9% 2.7% 2.5% 0.3%
Printing and Publishing 1.1% -1.9% 3.0% 4.8% -3.8% 8.5% -0.8% 1.4% -2.2%
Consumer Goods 0.7% 0.0% 0.7% -1.0% 1.7% -2.7% 1.7% -3.1% 4.7%
Apparel 0.3% 0.8% -0.5% -0.8% 2.8% -3.6% 0.7% -2.5% 3.2%
Medical Equipment 2.8% 1.9% 0.8% 4.0% 2.0% 2.0% 2.0% 1.9% 0.1%
Pharmaceutical Products 3.9% * 1.3% 2.6% 8.8% ** -0.4% 9.1% ** 1.2% 4.2% -3.0%
Chemicals 2.2% -1.1% 3.3% 2.9% 1.2% 1.8% 1.9% -4.9% 6.8% *
Textiles -2.5% -2.2% -0.3% -11.5% ** 2.4% -13.9% *** 3.1% -10.1% ** 13.2% ***
Construction Materials -0.4% -1.7% 1.3% -4.1% * -2.1% -2.0% 1.8% -1.1% 2.9%
Construction -8.1%*** 0.0% -8.1% * -12.8% ** -3.7% -9.1% -5.3% * 6.7% -12.0% **
Steel Works Etc -2.8% -3.8% * 1.0% -4.2% -2.0% -2.2% -2.0% -7.0% * 5.0%
Machinery -1.7% -0.2% -1.5% -1.8% 0.1% -1.9% -1.7% -0.8% -0.8%
Electrical Equipment 0.6% -0.8% 1.4% 0.5% -3.4% 3.9% 0.8% 3.8% -3.0%
Automobiles and Trucks -0.7% -1.1% 0.4% -2.1% 2.1% -4.2% 0.3% -6.8% ** 7.1%
Aircraft 0.8% 0.5% 0.3% 7.1% -1.6% 8.7% -2.5% 4.0% -6.5%
Shipbuilding, Railroad Equipment -4.1% -0.4% -3.7% -5.2% -3.0% -2.2% -3.4% 4.1% -7.5%
Non-Metallic and Industrial Metal Mining -0.2% 0.2% -0.4% -2.3% 2.5% -4.9% 1.0% -3.8% 4.8%
Coal -0.6% 5.6% -6.1% -5.6% 5.8% -11.4% * 2.1% 5.6% -3.5%
Petroleum and Natural Gas -0.1% 4.4% ** -4.5% -2.7% 3.3% -5.9% 1.5% 6.3% * -4.8%
Utilities 3.4% * -2.6% 6.0% * 4.8% -4.4% 9.1% ** 2.9% 0.1% 2.9%
Communication 4.1% ** -1.1% 5.1% ** 6.2% *** 0.2% 6.0% ** 2.8% -3.1% 5.9%
Business Services -2.9% 3.1% -6.0% -2.7% 2.7% -5.4% -3.4% * 4.5% -7.9% **
Computers 0.3% 2.3% -1.9% 14.7% *** 0.9% 13.8% *** -7.2% ** 4.9% -12.1% **
Electronic Equipment -6.5%*** 2.3% -8.8% ** -6.9% 2.6% -9.6% * -6.4% *** 1.9% -8.3%
Measuring and Control Equipment -0.9% 1.8% -2.8% 7.7% 2.1% 5.6% -5.9% ** 1.9% -7.9% *
Shipping Containers 5.3%*** -3.0% 8.3%*** 5.1% * 0.9% 4.3% 5.4% ** -9.5% ** 14.9% ***
Transportation -2.1% -2.2% 0.1% -6.6% ** -0.8% -5.8% 0.6% -4.7% 5.3%
Wholesale -8.1%*** 0.7% -8.8% ** -19.6% *** 0.3% -19.9% *** -0.8% 1.5% -2.3%
Retail 2.3% -0.9% 3.2% -1.6% 1.2% -2.8% 4.5% ** -4.4% 8.9% **
Restaurants, Hotels, Motels 1.4% -0.1% 1.5% 1.8% -1.1% 3.0% 1.1% 2.0% -0.9%
Banking 2.6% 3.1% -0.5% 2.7% 2.7% 0.0% 2.6% 3.8% -1.2%
Insurance -0.9% 0.2% -1.1% -4.5% -2.5% -1.9% 1.4% 4.8% -3.4%
Real Estate -9.5%*** -3.5% -6.0% -13.2% ** -6.6% -6.6% -7.2% ** 1.9% -9.1%
Trading -1.0% -1.2% 0.2% -4.7% -4.6% ** 0.0% 1.3% 4.9% -3.6%
Other -8.6%*** -0.2% -8.3% ** -8.1% 2.6% -10.8% * -8.9% *** -4.8% -4.1%
Personal Services -5.0% -1.6% -3.4% -3.0% -1.8% -1.2% -6.1% -1.1% -5.0%
Rubber and Plastic Products -0.2% 0.4% -0.7% -1.4% 1.3% -2.7% 0.4% -1.2% 1.6%
Candy & Soda 3.4% 0.3% 3.1% 4.1% -0.7% 4.7% 3.0% 1.3% 1.7%
Business Supplies -5.6% -1.9% -3.7% -21.6% ** -1.9% -19.7% * 4.0% * -2.2% 6.3%
Healthcare -10.1% * 4.3% -14.4% -10.1% * 4.9% -15.0%
Fabricated Products -7.1% ** -3.5% -3.5% -7.1% ** -4.5% -2.6%
Defense 2.2% 0.9% 1.2% 2.2% 2.7% -0.6%
Precious Metals -0.6% 0.6% -1.1% -0.6% -1.5% 0.9%
Significant at >10% 13 2 9 14 1 11 14 6 10
1926:07 to 2006:06 1926:07 to 1966:06 1966:07 to 2006:06
 99 
Table 3.4 Industry presidential cycles with a three-factor correction 
 
Notes: Table 3.4 reports regression results estimated with Equation 3.4 for industry outperformance over presidential 
cycles with a correction for general market movement, size (SMB), and value (HML) factors. The model equates to 
the Fama-French three-factor model with the inclusion of a political dummy variable. The political dummy variable 
(RPD) takes the value of one if a Republican is president and zero otherwise. The 0 intercept measures Democratic 
(DEM) outperformance, 1 measures marginal Republican outperformance (DIF), and (0 + 1) measures Republican (REP) outperformance. The table reports results for the full period and two equal sub-periods. The table indicates 
statistical significance at 1%***, 5%**, and 10%* based on Newey and West (1987) heteroskedasticity and 
autocorrelation corrected test-statistics. 
0 1 1 2 3( - )t t mt t t t tR RPD r rf SMB HML                      (Eq. 3.4) 
After factor adjustments and sub-period comparisons, relative industry 
outperformance between Republicans and Democrats dissipates entirely, with no 
Industry REP DEM DIF REP DEM DIF REP DEM DIF
Agriculture -4.0% * 0.6% -4.5% * -6.8% 1.1% -7.8% -4.2% -0.5% -3.8%
Food Products 5.8%*** -1.0% 6.8% 4.2% * -0.6% 4.7% * 5.2% ** -2.1% 7.4% *
Beer & Liquor 1.8% 1.3% 0.5% 0.8% 1.8% -1.0% 2.2% 0.4% 1.8%
Tobacco Products 8.9%*** -0.3% 9.2% 7.5% * -0.5% 8.0% 8.1% ** -0.4% 8.5%
Recreation -0.5% -8.8% ** 8.3% -0.3% -5.5% 5.2% -1.2% -14.6%*** 13.4%***
Entertainment -1.8% -3.6% 1.9% -7.8% * -5.8% -2.0% 1.9% 0.5% 1.5%
Printing and Publishing 0.9% -3.4% 4.3% 6.3% -5.5% 11.8% -2.3% 0.1% -2.4%
Consumer Goods 1.2% 0.3% 0.9% -0.7% 1.7% -2.4% 1.7% -2.5% 4.1%
Apparel 0.2% -1.2% 1.4% 0.9% 0.8% 0.1% -2.7% -5.4% 2.7%
Medical Equipment 3.4% 1.7% 1.7% 4.7% 1.2% 3.6% 4.7% * 3.0% 1.7%
Pharmaceutical Products 4.5% ** 2.4% 2.1% 8.6% ** 0.1% 8.6% * 3.9% * 6.9% ** -3.0%
Chemicals 2.0% -0.5% 2.5% 2.2% 2.3% * -0.1% -1.0% -5.8% ** 4.7%
Textiles -3.4% -5.0%*** 1.6% -9.8%*** 0.1% -9.9% ** -1.7% -13.8%*** 12.1%***
Construction Materials -0.6% -2.6% * 2.0% -3.5% -2.7% * -0.9% -1.3% -3.1% 1.8%
Construction -8.9%*** -2.9% -6.0% * -10.7% * -6.7% -4.0% -7.7%*** 3.9% -11.6% **
Steel Works Etc -3.9% * -5.5%*** 1.6% -3.6% -3.2% -0.3% -4.3% -9.3%*** 5.0%
Machinery -1.9% -1.4% -0.5% -1.2% -0.8% -0.4% -2.4% -2.3% -0.1%
Electrical Equipment 0.5% -0.5% 1.0% 0.1% -2.8% 2.9% 0.9% 4.0% -3.1%
Automobiles and Trucks -1.7% -1.9% 0.2% -2.0% 2.1% -4.1% -4.6% -9.1%*** 4.5%
Aircraft -0.1% -1.1% 1.0% 8.3% -3.0% 11.3% -5.6% * 1.8% -7.5%
Shipbuilding, Railroad Equipment -5.7% ** -2.3% -3.4% * -3.9% -4.9% * 0.9% -6.7% * 2.2% -8.9%
Non-Metallic and Industrial Metal Mining -0.5% -1.1% 0.6% -1.7% 1.7% -3.4% -2.3% -6.5% * 4.1%
Coal -0.9% 4.1% -5.0% -4.7% 4.1% -8.8% -0.5% 3.2% -3.7%
Petroleum and Natural Gas -1.4% 4.4% ** -5.8% ** -2.9% 3.4% -6.3% -1.2% 6.1% * -7.3% *
Utilities 1.8% -3.0% 4.9% 4.7% -4.2% 8.9% ** -1.6% -1.1% -0.5%
Communication 4.3% ** -0.4% 4.6% 6.0% ** 0.6% 5.4% * 2.1% -2.6% 4.7%
Business Services -1.2% 2.8% -4.0% -2.0% 1.8% -3.7% -0.1% 4.4% * -4.5%
Computers 2.4% 3.3% -0.9% 14.3%*** 1.6% 12.7%*** -1.8% 6.8% -8.5% *
Electronic Equipment -5.3% ** 1.5% -6.8% ** -6.0% 1.5% -7.5% -2.8% 2.0% -4.8%
Measuring and Control Equipment 1.5% 2.7% -1.2% 7.1% 3.2% 3.9% -3.0% 1.4% -4.4%
Shipping Containers 5.4%*** -2.8% 8.1% 5.1% * 1.1% 4.0% 4.5% ** -9.4% ** 13.9%***
Transportation -3.9% ** -4.0% ** 0.1% -5.7% * -2.5% -3.2% -2.3% -6.4% ** 4.1%
Wholesale -8.0%*** -1.0% -6.9% ** -18.3%*** -1.5% -16.9%*** -2.6% -0.6% -2.0%
Retail 2.9% -0.7% 3.6% -1.7% 1.6% -3.3% 3.7% * -5.1% * 8.8% **
Restaurants, Hotels, Motels 1.7% -1.1% 2.8% 2.7% -2.1% 4.8% -1.2% -0.1% -1.1%
Banking 2.2% 3.1% -1.0% 2.9% 2.8% 0.0% -1.4% 2.8% -4.2%
Insurance -1.9% 0.3% -2.2% -4.9% -1.9% -3.0% -1.8% 4.1% -5.9%
Real Estate -10.3%*** -6.8% ** -3.5% -11.6% * -8.7% ** -2.9% -12.0%*** -3.7% -8.3% *
Trading -2.2% -2.1% 0.0% -4.2% -5.3% ** 1.1% -1.4% 3.7% -5.1% *
Other -7.7%*** -1.1% -6.6% ** -7.1% 1.6% -8.8% -9.9%*** -6.4% -3.5%
Personal Services -4.7% -4.1% -0.6% -0.8% -4.7% 3.9% -7.8% ** -3.6% -4.2%
Rubber and Plastic Products -1.4% -1.9% 0.5% -1.7% -0.4% -1.2% -1.8% -4.4% 2.7%
Candy & Soda 2.1% -0.8% 2.9% 5.5% -0.9% 6.3% 0.9% 0.9% -0.1%
Business Supplies -8.1% * -5.6% -2.5% -21.1% * -6.7% -14.3% 1.3% -3.4% 4.7%
Healthcare -12.1% ** -0.4% -11.7% -11.8% ** 0.7% -12.5%
Fabricated Products -8.7% ** -6.5% -2.3% -8.7% ** -7.1% -1.5%
Defense -2.6% -2.6% 0.0% -2.5% -0.2% -2.3%
Precious Metals -2.8% -2.6% -0.1% -3.0% -4.5% 1.5%
Significant at >10% 17 7 7 13 5 7 14 12 10
1926:07 to 2006:06 1926:07 to 1966:06 1966:07 to 2006:06
 100 
industry showing consistent outperformance by political party across all periods. In 
particular, industry returns are highly sensitive to firm size under Democrats. This result 
is not surprising, given previous studies, such as Santa-Clara and Valkanov (2003), that 
document a pronounced small-firm effect under Democratic administrations. 
The study concludes that all remaining evidence of presidential cycles in industry 
returns disappears after correcting for industry sensitivity to size and value factors. 
Table 3.5 visually compares the statistical significance of industry performance 
estimated for all three models and for different sub-periods. It is clear from Panel A that 
a correction for relative market movement, with the modified single-index model, 
largely explains excess industry performance attributable to presidential cycles. Size 
and value factor sensitivity, controlled with a modified three-factor model, accounts for 
any remaining industry presidential cycle. Further, returns are not persistent across sub-
periods, as Panel B indicates. 
Table 3.5 Presidential cycle summary for different models and sub-periods 
 
Notes: Panel A of Table 3.5 reports a summary of statistically significant industry performance over presidential 
cycles for Republicans (REP), Democratic (DEM), and (REP-DEM) differences (DIF) for the indicated regression 
equations. Panel B reports a summary of statistically significant industry performance over presidential cycles for 
Republicans (REP), Democratic (DEM), and (REP-DEM) differences (DIF) for the indicated time-periods estimated 
with Equation 3.3. The political dummy variable (RPD) takes the value of one if a Republican is president and zero 
otherwise. The table indicates industry outperformance (+) or underperformance (-) statistically significant at 10 
percent or greater using Newey and West (1987) heteroskedasticity and autocorrelation corrected test-statistics. 
The results, to this point, suggest the political affiliation of a president has no effect 
on industry returns, beyond the expectations of rational investors. Consequently, the 
Panel A:
Ag
ric
Fo
od
Be
er
Sm
ok
e
To
ys
Fu
n
Bo
ok
s
Hs
hld
Cl
ths
Me
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Dru
gs
Ch
em
s
Tx
tls
Bld
Mt
Cn
str
Ste
el
Ma
ch
Elc
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Au
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Ae
ro
Sh
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Mi
nes
Co
al
Oil Uti
l
Te
lcm
Bu
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m p
s
Ch
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La
bE
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Bo
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Tra
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Wh
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Rta
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Me
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Ba
nk
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Ins
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Fin Oth
er
Per
Sv
Ru
bb
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So
da
Pa p
er
Hlt
h
Fab
P r
Gu
ns
Go
ld
RP + + + + +
DP + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DIF - - - - - - - - - - - - - - - - - - -
RP + + + - + + - + - - - - -
DP - +
DIF + + - + + - + - -
RP - + + + - - - + - + - - - - - - -
DP - - - - + - -
DIF + + + - + - + - -
Panel B:
Ag
ric
Fo
od
Be
e r
Sm
ok
e
To
ys
Fu
n
Bo
ok
s
Hs
hld
Cl
ths
Me
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q
Dr
ug
s
Ch
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s
Tx
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Bl
dM
t
Cn
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Ste
el
Ma
ch
Elc
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Au
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Ae
ro
Sh
ips
Mi
nes
Co
al
Oi
l
Ut
il
Te
lc m
Bu
sSv
Co
m p
s
Ch
i ps
La
bE
q
Bo
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Tra
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Wh
lsl
Rt
ail
Me
als
Ba
nk
s
Ins
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Rl
Est
Fin Ot
he r
Pe
rSv
Ru
bb
r
So
da
Pa
per
Hl
th
Fa
bP
r
Gu
ns
Go
ld
1926-2006 + + + - + + - + - - - - -
1926-1966 + + - + - - + + + - - - -
1966-2006 + + + + - - - + + - - - - -
1926-2006 - +
1926-1966 + - - - -
1966-2006 - + - - - - - + + - - -
1926-2006 + + - + + - + - -
1926-1966 + + - + + + -
1966-2006 + + + - - - + + - -
plus/minus denotes positive/negative significance at a level of 10% or greater
DP
DI
F 
Eq
. 3.
3
Eq
. 3.
1
Eq
. 3.
4
RP
plus/minus denotes positive/negative significance at a level of 10% or greater
 101 
study concludes no opportunity exists for timing industry investments with presidential 
cycles, beyond that already documented for the general market. Nominal industry 
returns are higher under Democrats. However, even then, observing industry returns 
relative to the market and additional risk factors, the presidential dummy variable loses 
explanatory power. The results provide evidence of a positive bias in industry returns 
for small-cap firms under Democratic administrations, as previous studies of major 
market indices document. The results do not support conventional wisdom, which holds 
particular industries perform better under a given political regime. For both Democratic 
and Republican administrations, investors are simply better off holding the market 
portfolio than specific industries or Treasury bills. 
3.5 Industry Quadrennial Cycles 
This section observes industry returns for evidence of quadrennial cycles. Systematic 
differences in industry returns between the first and second half of a four-year 
presidential term, irrespective of political affiliation, indicate quadrennial cycle. Prior 
studies find major U.S. stock market indices are typically higher during the second half 
of a presidential cycle. 
0 1 2t t tR H D                   (Eq. 3.2) 
Equation 3.2 tests for quadrennial cycle with a regression of excess industry returns 
(R) on a constant and the quadrennial cycle timing variable (H2D). 
Table 3.2 reports the results from Equation 3.2 for quadrennial cycles. Twenty 
industries, or approximately 42 percent of the industries, have statistically significant 
return differences (DIF) between the first half (HLF1) and second half (HLF2) of a 
presidential term. Higher returns occur in the last two years of an administration, for all 
industries. The largest return differences between first half and second half are for 
healthcare (35.0%) and aircraft (24.0%). The pharmaceutical (1.4%) and medical 
equipment (1.8%) industries have the smallest differences. Overall, there is no evident 
pattern across industries, with higher second-half returns observed in the primary, 
manufacturing, consumer staples, and consumer durables industries, in addition to both 
high and low beta industries. Figure 3.3 illustrates industry returns sorted left to right 
from highest second-half outperformance to highest first-half outperformance. 
 102 
Figure 3.3 Industry quadrennial cycles 
 
Notes: Figure 3.3 illustrates excess industry returns over quadrennial cycles estimated with Equation 3.2. The 
quadrennial cycle timing variable (H2D) takes the value of one during the second half of a four-year U.S. presidential 
cycle and zero otherwise. The table sorts excess returns from left to right by largest difference in returns between the 
second half and first half of a four-year U.S. presidential cycle. The 0 intercept measures average first-half returns 
and (0 + 1) measures average second-half industry returns. 
Notably, quadrennial cycles are more evident for unadjusted returns in the sub-period 
1966 to 2006, with statistically significant differences at level of 10 percent or greater in 
43 of the 48 industries. Stronger differences in second-half returns, during the latter 
period, are similar to the results of studies, such as Allvine and O'Neil (1980) and Booth 
and Booth (2003), that conjecture a more active role by government and politicians 
managing the post-WWII economy. Higher returns during the second half of a 
presidential administration support the contention that politicians are able to manipulate 
the economy to further their prospect of gaining reelection. 
As with presidential cycles, it is possible that the observed relative outperformance 
of second-half returns merely represents new market equilibrium rates of return. Risk 
and market volatility unquestionably increase with the uncertainty of an election and 
potential changes in the political agenda. Therefore, second-half returns may simply 
represent expected compensation for extra risk during the period prior to an election. 
Equation 3.5 controls for relative market movement in the quadrennial cycle model with 
 103 
the inclusion of a term for excess market returns. The model becomes the basic single-
index with Jensen’s alpha now split between first-half returns and second-half returns. 
0 1 12 ( - )t t mt t tR H D r rf                     (Eq. 3.5) 
Table 3.6 reports Equation 3.5 results. Relative to the market, the outperformance of 
second-half industry returns largely diminishes. Differences in returns remain positive 
and statistically significant in only two industries and negative in four. Interestingly, 
with the inclusion of a term for general market movement, a majority of industries 
actually show negative second-half returns, although statistically insignificant. Even in 
the latter sub-period, differences in returns between the first and second halves remain 
statistically significant in only five industries. The results indicate that, while industry 
returns are higher in the latter half of a presidency, outperformance dissipates after 
correcting for the market. Additionally, outperformance is not persistent across sub-
periods. 
  
 104 
Table 3.6 Industry quadrennial cycles with a market correction 
 
Notes: Table 3.6 reports regression results estimated with Equation 3.5 for industry outperformance over quadrennial 
cycles corrected for general market movement. The model equates to a single-index model with the inclusion of a 
quadrennial cycle timing variable (H2D) that takes the value of one during the second half of a four-year U.S. 
presidential cycle and zero otherwise. The 0 intercept measures first half (HLF1) outperformance, 1 measures 
marginal second half outperformance (HLF2), and (0 + 1) measures second half outperformance. The table reports results for the full period and two equal sub-periods. The table indicates statistical significance at 1%***, 5%**, and 
10%* based on Newey and West (1987) heteroskedasticity and autocorrelation corrected test-statistics. 
Lastly, Equation 3.6 includes the Fama and French (1993) size (SMB) and value 
(HML) factors, to control for any possible small-cap or value effects in quadrennial 
cycles. The model now becomes a modified three-factor model. 
Industry HLF1 HLF2 DIF HLF1 HLF2 DIF HLF1 HLF2 DIF
Agriculture -0.3% -2.2% -1.9% 1.7% -5.3% -7.0% -2.1% 1.0% 3.1%
Food Products 3.2% ** 1.7% -1.5% 1.6% 0.5% -1.2% 4.5% * 3.4% -1.2%
Beer & Liquor 6.1% * -1.5% -7.6% * 7.9% -4.0% -11.9% * 3.2% 2.4% -0.8%
Tobacco Products 6.0% ** 2.7% -3.3% 3.1% 1.5% -1.6% 9.3% ** 3.6% -5.8%
Recreation -7.6% * 0.4% 8.0% -7.1% 1.7% 8.8% -8.5% ** -0.5% 8.0%
Entertainment 1.5% -5.2% * -6.7% 0.8% -12.8%*** -13.6% ** 1.5% 3.9% 2.4%
Printing and Publishing 3.0% -3.7% -6.7% 4.8% -6.0% -10.9% 1.0% -1.0% -2.0%
Consumer Goods 1.0% -0.4% -1.4% 0.4% 1.1% 0.8% 1.7% -1.8% -3.5%
Apparel -0.4% 1.4% 1.8% 4.5% -1.4% -5.9% -4.2% 3.5% 7.6%
Medical Equipment 5.7% ** -0.9% -6.6% * 8.4% ** -2.7% -11.1% ** 3.4% 0.6% -2.8%
Pharmaceutical Products 6.1%*** -0.9% -7.0% ** 6.1% * -0.3% -6.4% 5.9% ** -1.3% -7.2% *
Chemicals 1.5% -0.4% -1.9% 4.0% ** -0.4% -4.3% -1.2% -0.1% 1.1%
Textiles -1.1% -3.7% -2.5% 0.0% -5.7% * -5.6% -3.3% -0.4% 2.9%
Construction Materials 0.1% -2.2% -2.3% -0.7% -5.0%*** -4.3% * 0.6% 0.9% 0.3%
Construction -4.8% -3.5% 1.3% -6.3% -7.9% -1.5% -3.6% 1.5% 5.1%
Steel Works Etc -3.5% -3.2% 0.3% -1.5% -4.1% -2.5% -5.9% -1.7% 4.1%
Machinery -1.1% -0.9% 0.3% -0.2% -1.1% -0.9% -2.3% -0.3% 2.0%
Electrical Equipment 0.5% -0.7% -1.2% -2.5% -1.5% 1.0% 2.9% 0.8% -2.1%
Automobiles and Trucks -1.4% -0.4% 1.0% -1.0% 2.1% 3.0% -3.1% -1.4% 1.7%
Aircraft -4.0% 5.6% 9.6% * -4.9% 8.4% 13.3% -4.4% 4.2% 8.7%
Shipbuilding, Railroad Equipment -2.1% -2.4% -0.3% -1.5% -6.0% * -4.4% -3.6% 2.3% 5.8%
Non-Metallic and Industrial Metal Mining -2.6% 2.7% 5.3% 0.3% 1.2% 1.0% -5.1% 3.9% 9.0%
Coal -1.2% 6.2% 7.4% -3.2% 6.5% 9.7% 3.1% 3.6% 0.6%
Petroleum and Natural Gas 0.7% 3.6% * 3.0% 1.0% 1.1% 0.1% -0.5% 7.0% ** 7.5% *
Utilities -1.0% 1.7% 2.7% -4.5% 2.5% 7.0% 0.6% 3.2% 2.7%
Communication 2.1% 0.9% -1.3% 0.1% 4.7% ** 4.6% * 5.0% * -3.8% -8.8% **
Business Services 1.2% -1.0% -2.2% 4.3% -2.7% -7.0% 1.1% -2.4% -3.5%
Computers 2.0% 0.5% -1.5% 7.6% ** 3.9% -3.8% -2.4% -3.6% -1.3%
Electronic Equipment -1.3% -3.1% -1.8% 2.3% -4.1% -6.4% -4.1% -2.9% 1.3%
Measuring and Control Equipment 1.0% -0.1% -1.1% 11.2%*** -2.6% -13.8%*** -5.9% * -0.3% 5.6%
Shipping Containers 3.9% * -1.7% -5.7% ** 5.8% ** -0.9% -6.7% ** 2.1% -2.6% -4.7%
Transportation -0.9% -3.4% * -2.5% -0.4% -5.4% ** -5.0% -1.8% -0.9% 0.9%
Wholesale -2.3% -5.2% * -2.9% -3.0% -11.8% ** -8.8% -1.8% 1.9% 3.6%
Retail 0.4% 1.0% 0.6% -1.5% 2.0% 3.5% 2.9% -0.6% -3.5%
Restaurants, Hotels, Motels -0.9% 2.3% 3.2% 2.2% -2.3% -4.5% -3.1% 6.2% 9.3% *
Banking 3.4% 2.3% -1.1% 4.3% 1.1% -3.2% 2.2% 3.9% 1.7%
Insurance 1.6% -2.3% -3.9% -0.9% -5.6% -4.7% 2.5% 2.8% 0.4%
Real Estate -8.3% ** -4.8% 3.6% -8.9% * -9.3% -0.4% -9.0% ** 1.3% 10.3% *
Trading -3.7% * 1.6% 5.3% ** -8.6%*** -0.5% 8.0% ** 0.2% 5.0% ** 4.8%
Other -5.3% * -3.6% 1.7% 1.1% -3.9% -5.0% -10.5%*** -4.2% 6.4%
Personal Services -4.8% -1.7% 3.1% -4.9% 0.5% 5.4% -4.5% -4.1% 0.4%
Rubber and Plastic Products 1.1% -0.9% -2.1% 1.8% -1.0% -2.9% -0.3% 0.0% 0.3%
Candy & Soda 3.8% -0.3% -4.1% -0.9% 2.2% 3.1% 6.9% * -2.0% -8.9%
Business Supplies -0.3% -7.1% * -6.8% -5.6% -12.0% * -6.4% 1.2% 2.3% 1.1%
Healthcare -11.5% 3.1% 14.6% -18.9% 4.2% 23.1% -11.1% 1.8% 13.0%
Fabricated Products -8.4% ** -2.8% 5.6% 0.7% -2.8% -3.5% -9.1% ** -3.1% 6.0%
Defense 3.1% 0.2% -2.9% 15.3% * -31.3%*** -46.5%*** 2.2% 2.6% 0.4%
Precious Metals 2.3% -2.5% -4.9% 7.2% 27.3% 20.1% 2.1% -3.9% -6.0%
Significant at  >10% 11 5 6 9 9 9 10 2 5
1926:07 to 2006:06 1926:07 to 1966:06 1966:07 to 2006:06
 105 
0 1 1 2 32 ( - )t t mt t tR H D r rf SMB HML                      (Eq. 3.6) 
Table 3.7 Industry quadrennial cycles with a three-factor correction 
 
Notes: Table 3.7 reports regression results estimated with Equation 3.6 for industry outperformance over quadrennial 
cycles with a correction for general market movement, size (SMB), and value (HML) factors. The model equates to 
the Fama-French three-factor model with the inclusion of a quadrennial cycle timing variable (H2D) that takes the 
value of one during the second-half of a four-year U.S. presidential cycle and zero otherwise. The 0 intercept 
measures first-half (HLF1) outperformance, 1 measures marginal second half outperformance (HLF2), and (0 + 1) measures second-half outperformance. The table reports results for the full period and two equal sub-periods. The 
table indicates statistical significance at 1%***, 5%**, and 10%* based on Newey and West (1987) 
heteroskedasticity and autocorrelation corrected test-statistics. 
Table 3.7 reports the results from Equation 3.6. While differences remain significant 
in eight industries for the entire period, they lack persistence across sub-periods. 
Industry HLF1 HLF2 DIF HLF1 HLF2 DIF HLF1 HLF2 DIF
Agriculture -0.5% -2.9% -2.4% 1.7% -5.3% -7.0% -2.6% -3.1% -0.4%
Food Products 2.9% * 1.9% -1.0% 1.8% 0.6% -1.2% 2.9% 2.1% -0.8%
Beer & Liquor 5.5% * -2.3% -7.8% * 7.4% -4.3% -11.7% * 1.9% 1.1% -0.8%
Tobacco Products 5.5% * 2.9% -2.6% 3.2% 1.6% -1.6% 7.5% * 2.4% -5.1%
Recreation -7.8% ** -1.5% 6.2% -8.0% 1.0% 9.0% -8.7% ** -3.7% 5.1%
Entertainment 1.0% -6.3% ** -7.3% * 0.4% -13.1%*** -13.5% ** 1.6% 1.2% -0.3%
Printing and Publishing 2.4% -4.9% * -7.3% * 4.1% -6.5% -10.7% 0.2% -3.1% -3.3%
Consumer Goods 1.5% -0.1% -1.6% 0.4% 1.2% 0.8% 1.4% -1.2% -2.6%
Apparel -0.9% -0.1% 0.8% 3.7% -2.0% -5.7% -5.8% * -1.4% 4.4%
Medical Equipment 6.3%*** -1.0% -7.3% ** 8.1% ** -2.9% -11.0% ** 5.1% * 3.0% -2.1%
Pharmaceutical Products 7.0%*** 0.0% -7.0% ** 6.4% ** -0.1% -6.5% 7.1%*** 2.7% -4.4%
Chemicals 1.5% 0.1% -1.4% 4.5% ** 0.1% -4.5% -3.2% -2.4% 0.8%
Textiles -2.6% -5.9%*** -3.3% -0.9% -6.3% ** -5.4% -5.6% * -7.0% ** -1.4%
Construction Materials -0.3% -2.9% * -2.6% -0.9% -5.1%*** -4.2% * -1.1% -2.9% -1.7%
Construction -6.2% * -5.8% * 0.4% -7.5% -8.8% ** -1.3% -4.6% -2.6% 1.9%
Steel Works Etc -4.9% ** -4.5% ** 0.3% -2.1% -4.5% * -2.4% -6.8% * -5.5% * 1.3%
Machinery -1.5% -1.7% -0.2% -0.5% -1.4% -0.9% -2.4% -2.3% 0.1%
Electrical Equipment 0.5% -0.5% -1.0% -2.2% -1.3% 1.0% 2.9% 1.1% -1.8%
Automobiles and Trucks -2.5% -1.1% 1.4% -0.9% 2.1% 3.1% -6.1% * -6.3% ** -0.2%
Aircraft -5.2% 4.2% 9.4% * -5.4% 7.9% 13.4% -6.1% * 0.5% 6.6%
Shipbuilding, Railroad Equipment -4.0% -4.0% 0.0% -2.4% -6.6% ** -4.3% -5.5% -1.4% 4.2%
Non-Metallic and Industrial Metal Mining -3.2% 1.6% 4.8% -0.1% 1.0% 1.0% -6.7% -0.8% 5.9%
Coal -1.7% 5.0% 6.8% -3.9% 5.8% 9.8% 1.8% -0.2% -2.0%
Petroleum and Natural Gas -0.6% 3.5% * 4.1% 1.0% 1.1% 0.0% -2.5% 5.6% * 8.1% *
Utilities -2.5% 1.3% 3.8% -4.5% 2.5% 7.0% -2.5% -0.2% 2.3%
Communication 2.5% 1.4% -1.1% 0.3% 4.9%*** 4.6% * 4.3% -3.5% -7.8% *
Business Services 2.8% -1.2% -4.0% 3.9% -3.0% -6.9% 3.9% * -0.9% -4.8% *
Computers 4.2% * 1.4% -2.8% 8.0%*** 4.2% -3.8% 1.5% 1.0% -0.6%
Electronic Equipment -0.3% -3.7% -3.4% 1.8% -4.4% -6.3% -1.2% -1.0% 0.2%
Measuring and Control Equipment 3.5% 0.7% -2.9% 11.8%*** -2.1% -13.9%*** -3.4% 0.6% 4.0%
Shipping Containers 4.1% ** -1.5% -5.6% ** 6.0%*** -0.8% -6.7% ** 1.3% -3.0% -4.3%
Transportation -3.0% * -4.9%*** -1.9% -1.3% -6.1% ** -4.8% -3.5% -4.2% * -0.8%
Wholesale -2.6% -6.6% ** -4.0% -3.6% -12.2% ** -8.6% -2.5% -1.2% 1.2%
Retail 1.0% 1.2% 0.2% -1.3% 2.2% 3.5% 2.5% -1.8% -4.3%
Restaraunts, Hotels, Motels -0.9% 1.5% 2.4% 1.9% -2.5% -4.4% -4.1% 2.8% 6.9%
Banking 3.0% 2.3% -0.7% 4.4% 1.3% -3.2% -0.6% 0.9% 1.5%
Insurance 0.6% -2.3% -2.9% -0.6% -5.4% -4.8% 0.1% 0.5% 0.4%
Real Estate -9.7%*** -7.3% ** 2.4% -9.7% ** -9.9% * -0.2% -10.9%*** -7.0% ** 3.9%
Trading -5.0%*** 0.8% 5.7% ** -8.9%*** -0.8% 8.1% ** -1.5% 2.5% 4.0%
Almost Nothing -4.7% * -4.2% 0.4% 0.8% -4.1% -4.8% -10.8%*** -6.4% 4.4%
Personal Services -5.1% -3.7% 1.4% -6.3% -0.4% 5.9% -5.0% -7.6% * -2.6%
Rubber and Plastic Products -0.3% -3.1% -2.8% 0.2% -1.9% -2.1% -0.9% -4.7% * -3.7%
Candy & Soda 2.5% -1.4% -3.9% -0.9% 2.7% 3.7% 5.2% -3.5% -8.7%
Business Supplies -3.2% -10.5% ** -7.2% -9.4% -13.9% * -4.5% -0.6% -0.3% 0.2%
Healthcare -12.6% * -1.8% 10.9% -36.9% 18.2% 55.1% -11.1% -3.4% 7.7%
Fabricated Products -9.4% ** -6.1% * 3.3% -10.6% 1.3% 11.8% -9.4% ** -6.7% * 2.7%
Defense 0.0% -5.1% -5.1% -7.9% -23.3% ** -15.4% -0.6% -2.8% -2.2%
Precious Metals 0.8% -6.3% -7.1% 10.9% 21.6% 10.7% 1.2% -8.4% -9.6%
Significant at >10% 16 12 8 8 12 8 13 9 3
1926:07 to 2006:06 1926:07 to 1966:06 1967:06 to 2006:06
 106 
Notably, in the most recent 40 years, only three portfolios show a statistically 
significant difference, with only one of these positive. The inclusion of size and value 
factors fails to support a finding of quadrennial cycles in industry returns. 
To summarize, there is no evidence of quadrennial cycles in industry returns, after 
correcting for relative market movement and additional risk factors. The appearance of 
excess returns during the second half of a four-year presidential administration simply 
represents expected returns for bearing additional risk. Table 3.8 visually compares the 
statistical significance of first-half and second-half returns, across different models and 
for different sub-periods. It is clear from Panel A that what initially appears as a strong 
indication of quadrennial cycles from the basic model, results dissipate or become 
negative with a correction for additional risk factors. Additionally, returns are not 
persistent across sub-periods, as Panel B illustrates. 
Table 3.8 Quadrennial cycle summary for different models and sub-periods 
 
Notes: Panel A of Table 3.8 reports a summary of statistically significant industry performance over quadrennial 
cycles for the first half (HLF1), second half (HLF2), or (HLF2-HLF1) difference (DIF) for the indicated regression 
equations. Panel B reports a summary of statistically significant industry performance over quadrennial cycles for the 
first half (HLF1), second half (HLF2), or (HLF2-HLF1) difference (DIF) estimated with Equation 3.5. The 
quadrennial cycle dummy variable (H2D) takes the value of one during the second half of a four-year U.S. 
presidential cycle and zero otherwise. The table indicates industry outperformance (+) or underperformance (-) 
statistically significant at 10 percent or greater using Newey and West (1987) heteroskedasticity and autocorrelation 
corrected test-statistics. 
Panel A:
Ag
ric
Fo
od
Be
er
Sm
ok
e
To
ys
Fu
n
Bo
ok
s
Hs
hld
Cl
ths
Me
dE
q
Dr
ug
s
Ch
em
s
Tx
tls
Bl
dM
t
Cn
str
Ste
el
Ma
ch
Elc
Eq
Au
tos
Ae
ro
Sh
ips
Mi
ne
s
Co
al
Oi
l
Ut
il
Te
lcm
Bu
sSv
Co
m p
s
Ch
ips
La
bE
q
Bo
xe
s
Tra
ns
Wh
lsl
Rt
ail
Me
als
Ba
nk
s
Ins
ur
Rl
Es
t
Fin Ot
he
r
Pe
rSv
Ru
bb
r
So
da
Pa
pe
r
Hl
th
Fa
bP
r
Gu
ns
Go
ld
HLF1 + + + - -
HLF2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DIF + + + + + + + + + + + + + + + + + + + +
HLF1 + + + - + + + - - - -
HLF2 - + - - -
Diff - - - + - +
HLF1 + + + - + + - - + + - - - - - -
HLF2 - - - - - - + - - - - -
DIF - - - - - + - +
Panel B:
Ag
ric
Fo
od
Be
er
Sm
ok
e
To
ys
Fu
n
Bo
ok
s
Hs
hld
Cl
ths
Me
dE
q
Dr
ug
s
Ch
em
s
Tx
tls
Bl
dM
t
Cn
str
Ste
el
Ma
ch
Elc
Eq
Au
tos
Ae
ro
Sh
ips
Mi
ne
s
Co
al
Oi
l
Ut
il
Te
lcm
Bu
sSv
Co
m p
s
Ch
ips
La
bE
q
Bo
xe
s
Tra
ns
Wh
lsl
Rt
ail
Me
als
Ba
nk
s
Ins
ur
Rl
Est
Fin Ot
her
Pe
rSv
Ru
bb
r
So
da
Pa
per
Hl
th
Fa
bP
r
Gu
ns
Go
ld
1926-2006 + + + - + + + - - - -
1926-1966 + + + + + + - - +
1966-2006 + + - + + - - - + -
1926-2006 - + - - -
1926-1966 - - - - + - - - -
1966-2006 + +
1926-2006 - - - + - +
1926-1966 - - - - + - - + -
1966-2006 - + - + +
plus/minus denotes positive/negative significance at a level of 10% or greater
HL
F2
DIF
 
Eq
. 3.
5
Eq
. 3
.2
Eq
. 3
.6
HL
F1
plus/minus denotes positive/negative significance at a level of 10% or greater
 107 
3.6 Event Study Results  
This section examines whether expected differences in industry performance resulting 
from election outcomes support the finding that presidential politics have no systematic 
effect on realized industry returns. An event study of the 2004 presidential election 
provides a measure of differences in expected industry performance. Most other 
elections have highly probable outcomes well in advance of Election Day, making it 
difficult to pick a date when the market prices election resolution. The closely contested 
2004 election provides a unique opportunity to measure the effect of election outcomes 
on expected differences in industry performance. If, for example, expected industry 
performance differs systematically due to the election of a Democratic versus a 
Republican president, industry prices should reflect that difference immediately with 
election resolution. Expected industry performance may differ, for example, due to the 
systematic effect of political agendas on industry cash flows. Alternatively, differences 
in expected returns may merely reflect unsubstantiated beliefs that certain industries 
perform better under Democratic or Republican presidents. Earlier results show that 
stock market returns are systematically higher under Democratic presidents. 
Consequently, market efficiency suggests that the general stock market should gain 
value following news of a Democratic victory and lose value following news of a 
Republican victory. Prior results also show that a president’s political affiliation has no 
systematic bearing on differential industry performance. Market efficiency further 
suggests that election outcomes should have no effect on expected industry returns, 
despite the popular belief that political cycles drive industry performance. 
Two of the closest presidential elections in the past century, based on popular vote, 
were the 2000 Bush-Gore election and the 2004 Bush-Kerry election.52 Bush ultimately 
won the 2000 election by a 0.5% popular vote margin. However, his official election 
victory awaited a U.S. Supreme Court decision, which resolved the contested Florida 
vote count. The stock market could have potentially factored in a Bush victory on 
several occasions over the 36 days between the election and the 11 December 2000 
Supreme Court decision. For instance, an uncertified Florida ballot count on 18 
                                                 
52 http://www.mit.edu/~mi22295/elections.html 
 108 
November gave the victory to Bush. On 26 November, the Florida Election 
Commissioner officially certified a Bush victory. In response, Bush immediately formed 
a transition team. Gallup Poll surveys, conducted after the election until 6 December, 
show Al Gore’s public support steadily dropped from 52 to 39 percent.53 Thus, 
pinpointing when stock prices reflect differences in expected returns in response to the 
2000 election is difficult to determine. 
The 2004 Bush-Kerry presidential campaign ranks as the next closest U.S. 
presidential election. Only a 2.4% difference in the popular vote determined a 
Republican victory. The 2004 Bush-Kerry presidential campaign was extremely close, 
from the campaign outset until voting closed on Election Day. According to Larry 
Sabato, director of the University of Virginia's Center for Politics, "Only a fool would 
have bet on [the 2004] election."54 Uncharacteristically, neither Democratic nor 
Republican conventions generated an appreciable jump in candidate support. As the 
election drew near, polls showed the Democratic and Republican candidates virtually 
tied, as Table 3.9 details.55 For instance, a Fox News poll two days prior to the election 
shows Kerry 3 percent ahead of Bush. Exit polls, conducted by the National Election 
Pool, continued to show Kerry 3 percent ahead in the popular vote on Election Day.56 
The election was so close that major news networks waited to proclaim a Bush victory 
until the morning after Election Day. Democratic candidate John Kerry conceded the 
race two hours before the stock market opened on the morning after the election. 
  
                                                 
53 http://www.gallup.com/poll/2242/gores-image-slipping-election-contest-drags.aspx 
54 http://www.businessweek.com/magazine/content/04_26/b3889624.htm 
55 The data comes from Polling Reports at http://www.pollingreport.com/wh04two.htm 
56 http://www.ropercenter.uconn.edu/elections/presidential/presidential_election_2004.html 
 109 
 
Table 3.9 Poll results for the 2004 presidential election 
 
Notes: Table 3.9 reports the results of polls over 30-days prior to the 2004 presidential election.57 
As confirmation of the close election, Figure 3.4 compares the poll results with the 
Iowa Electronic Market (IEM) percentage of vote probabilities. Prediction markets, 
such as the IEM, take nominal bets that translate into electoral outcome probabilities.58 
Studies such as Berg, Nelson, and Rietz (2008) document that prediction markets 
accurately forecast election outcomes. Additionally, Snowberg, Wolfers, and Zitzewitz 
(2008) document the importance of political prediction markets in assisting researchers 
to pinpoint event dates. Figure 3.4 illustrates the percentage vote share for Republican 
candidate George Bush over a three-month period before the 2004 election. As with 
election polls, IEM probabilities depict a closely contested campaign, showing John 
Kerry ahead on Election Day. Both election poll statistics and IEM probabilities support 
the selection of the 2004 election for an event study. 
                                                 
57 Source http://www.pollingreport.com/wh04two.htm 
58 For complete information on the Iowa Electronic Market see http://www.biz.uiowa.edu/iem/index.cfm. 
Poll Survey Date Bush (%) Kerry (%) Bush (%) Kerry (%)
FOX/Opinion Dynamics 31/10 45 48 3
CNN/USA Today/Gallup 31/10 47 48 1
FOX/Opinion Dynamics 30/10 45 47 2
American Research Group 30/10 48 49 1
FOX/Opinion Dynamics 29/10 46 46
Newsweek 29/10 48 45 3
FOX/Opinion Dynamics 28/10 47 47
CNN/USA Today/Gallup 24/10 49 48 1
Los Angeles Times 24/10 47 48 1
Newsweek 22/10 47 47
CNN/USA Today/Gallup 16/10 50 46 4
Newsweek 15/10 48 47 1
Time 15/10 46 46
Scripps Center, Ohio U. 13/10 45 50 5
ICR 11/10 48 46 2
CNN/USA Today/Gallup 10/10 48 48
Time 07/10 43 44 1
ICR 05/10 50 46 4
American Research Group 04/10 45 48 3
CNN/USA Today/Gallup 03/10 49 48 1
Newsweek 02/10 46 49 3
MarginRegistered Voters
 110 
Figure 3.4 Election probabilities for the 2004 presidential election 
 
Notes: Figure 3.4 illustrates the Iowa Electronic Market probability of a Republican victory in the 2004 Bush-Kerry 
presidential election.59 The period begins 1 August 2004 upon conclusion of the Democratic nominating conference 
and ends on Election Day 2 November 2004. 
Distinct Bush-Kerry political agendas had clear implications for industry cash flows 
and profitability. Comparatively, centrist candidate positions made the political agendas 
of other recent elections indistinguishable (Hale (1995)). However, in 2004, 
Republicans strongly proposed tax-advantaged investments, domestic oil exploration, 
and expanded privatization of healthcare provision. Meanwhile, the Democrats 
promoted taxes on the wealthy, alternative energy, tighter emission controls, and 
affordable housing (Kim, (2004)). As such, the energy extraction, automotive, and 
investment industries particularly stood to benefit from a Republican victory. 
Conversely, a Democratic victory benefited industries such as alternative energy and 
construction. A combination of contrasting political agendas and a closely contested 
2004 Bush-Kerry election provides a unique event study to measure the market’s 
response to differences in Democratic and Republican policy agendas. 
                                                 
59 See http://www.biz.uiowa.edu/iem/closed/Pres04_VS.html for complete details on political stock markets. 
30%
35%
40%
45%
50%
55%
60%
65%
70%
01/08 08/08 15/08 22/08 29/08 05/09 12/09 19/09 26/09 03/10 10/10 17/10 24/10 31/10
Probability of a Republican victory
Probability of a Bush victory
Republican 
Convention
Democratic 
Convention
Election Day
 111 
Previous event studies examine how broad-market indices respond to presidential 
elections, with mixed results. Niederhoffer, Gibbs, and Bullock (1970) observe Dow 
Jones Industrial Average (DJIA) changes over elections held from 1900 to 1968. The 
DJIA increases an average 1.12 percent for eight of nine Republican victories and it 
decreases an average -0.81 for four of five Democratic victories. Similarly, Riley and 
Luksetich (1980) find the DJIA increases following Republican victories for elections 
held from 1927 to 1998. Conversely, Santa-Clara and Valkanov (2003) observe no 
immediate change in excess returns, for either political party, following elections held 
from 1927 to 1998. Rather, Santa-Clara and Valkanov (2003) observe excess returns 
gradually build over four-year presidential terms, particularly for Democrats. 
Other research documents the response of particular sectors to election news. 
Homaifar, Randolph, Helms, and Haddad (1988), covering six elections from 1964 to 
1984, examine price changes to defense industry stocks. They conclude stock price 
sensitivity relates to candidate policy and not political affiliation. Similarly, Roberts 
(1990) evaluates the sensitivity of defense firm values to changes in 1980 Reagan-
Carter election probabilities. Firm values have no systematic relationship with election 
outcomes, which Roberts (1990) attributes to indistinguishable candidate defense 
policies in 1980. 
Herron, Lavin, Cram, and Silver (1999) examine Dow Jones indices returns for the 
1992 election. They argue aggregate market indices fail to reveal priced political 
outcomes. A Democratic victory in 1992 affects only 20 percent of Dow Jones industry 
returns. Pollution control, aerospace, defense, pharmaceuticals, personal care, and 
financial services were the most affected industries. Interestingly, defense and 
pharmaceuticals are traditional Republican industries (Kim (2004)). Herron, Lavin, 
Cram, and Silver (1999) conclude the 1992 election results are candidate rather than 
party specific. Knight (2006) evaluates the relation of individual stock performance to 
changes in election probabilities. His sample includes 70 firms expected to benefit 
under either Bush or Gore 2000 presidencies. Knight identifies pharmaceutical, defense, 
energy, tobacco, and Microsoft competitors as “policy sensitive” stocks. Similar to 
Herron, Lavin, Cram, and Silver (1999), his study shows the market capitalizes 
candidate rather than party platforms. 
 112 
The event-study analysis groups the Fama-French 48 industries into Democratic or 
Republican oriented portfolios based on three criteria.60 These include past 
performance, political campaign contributions, and Wall Street analysts’ projections. 
Each group provides different indicators of potential differences in expected industry 
performance. Table 3.10 provides details of portfolio constituent industries. 
The best-performing Democratic and Republican portfolios comprise the top 16 of 
48 industries that provide each party with the greatest relative performance differences 
(DIF), as Table 3.4 reports. These portfolios provide a measure of investor attention to 
realized industry performance over previous political cycles. If investors use election 
outcomes as a signal of expected industry returns, the results should indicate significant 
differences in excess returns for the best-performing Republican industries upon 
resolution of the 2004 election. The analysis includes a neutral portfolio that comprises 
the remaining 16 of 48 Fama-French industries. 
Next, the analysis evaluates portfolios based on total industry campaign 
contributions, either to the political party or directly to the candidate. The 2004 election 
marks the first presidential election under the 2002 Bipartisan Campaign Reform Act. 
This legislation requires extensive reporting of all campaign contributions. Contribution 
data is available through the Center for Responsive Politics.61 Campaign contributions 
provide a measure of industry investment in political outcomes. Presumably, industries 
anticipate returns in the form of improved profitability or cash flows from their 
campaign contributions. Investors could thus interpret campaign contributions as a 
signal of expected industry performance contingent on election results. The event study 
analysis includes the top 16 of 48 industries contributing to the Republican campaign in 
one portfolio and likewise for the Democrats.62 
                                                 
60 Daily return data for the Fama-French 48 industries comes from the Kenneth French website 
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
61 http://www.opensecrets.org/ 
62Some industries appear to hedge their political bets with almost equivalent contributions to Republicans and 
Democrats. 
 113 
Table 3.10 Event study groupings for the 2004 presidential election 
 
Notes: Table 3.10 details groupings of Fama-French 48 industries used to form event study portfolios. The best-
performing Democratic and Republican industries are the top 16 industries for each party with the greatest risk 
adjusted performance differences (DIF) from Table 3.4. The remaining 16 industries comprise a neutral portfolio. 
Industries grouped by contributions are the top 16 industries each for Democrats and Republicans receiving the 
highest political contributions. Lastly, the last column lists the 16 industries Wall Street analysts forecast to benefit 
most under a Republican presidency. 
  
Analaysts
Industry Democrat Republican Neutral Democrat Republican Republican
Agriculture D
Aircraft N
Apparel N
Automobiles and Trucks N R R
Banking N D R R
Beer & Liquor N
Business Services D D R
Business Supplies D
Candy & Soda R
Chemicals R R
Coal D R
Communication R N
Computers D D R
Construction D D R
Construction Materials R D
Consumer Goods N
Defense R D R
Electrical Equipment R
Electronic Equipment D
Entertainment N D R
Fabricated Products D D R
Food Products R
Healthcare D D R R
Insurance N D R R
Machinery N
Measuring and Control Equipment D
Medical Equipment N R
Non-Metallic and  Metal Mining N R
Other D
Personal Services D
Petroleum and Natural Gas D R R
Pharmaceutical Products R D R R
Precious Metals N
Printing and Publishing R D R
Real Estate D D R
Recreation R N
Restaurants, Hotels, Motels R R
Retail R D R R
Rubber and Plastic Products
Shipbuilding, Railroad Equipment D
Shipping Containers R
Steel Works Etc R R
Textiles N
Tobacco Products R
Trading D R R
Transportation N
Utilities R D R R
Wholesale D
Total: 16 16 16 16 16 16
Most ContributionsBest Performing
 114 
Lastly, the analysis evaluates a portfolio comprised of 16 industries that, according to 
a consensus of Wall Street analysts, would benefit from a Republican presidency.63 
Analysts base their projections on 2004 presidential campaign promises. The analysts’ 
projections provide a signal of the Republican Party’s intended economic policies, 
which could affect differences in expected industry performance. 
The event study follows common methodology outlined in MacKinlay (1997) and 
Binder (1998). The event window includes 20 trading days on either side of 3 
November 2004, the day following the election. In total, the event window covers 41 
trading days starting 7 October 2004 and ending 3 December 2004. First, Equation 7 
estimates parameters for a single-index model over 250 trading days prior to the event 
window. As the different strategy portfolios comprise industries held in equal weights, 
the appropriate benchmark to gauge excess performance is an equal-weighted index. 
Campbell and Wasley (1993) also recommend the use of an equal-weighted index in 
event studies that use the market model to measure abnormal returns.64 
pt i i mt itr r                     (Eq. 3.7) 
The market model (Equation 3.7) regresses the returns for each industry grouping (ri ) 
on a constant (i) and an equal-weighted market index (rm), estimated from 9 October 
2003 to 6 October 2004. Next, Equation 3.8 calculates daily abnormal returns (ARi) over 
the event window as the error terms from the estimated single-index model. Then, 
Equation 3.9 calculates equal-weighted average abnormal returns (AARp) from the N 
industries included in each event study portfolio. Lastly, Equation 3.10 calculates 
cumulative average abnormal returns (CAARp) as aggregate average abnormal returns 
over different t1 to t2 windows. 
ˆˆ( )it it i i mtAR r r                  (Eq. 3.8) 
1
1 N
pt it
i
AAR ARN                  (Eq. 3.9) 
                                                 
63 Kim (2004). 
64 Results are comparable whether the performance benchmark uses an equal-weighted or value-weighted index.  
 115 
2
1
1 2( , )
t
p pt
t t
CAAR t t AAR


            
(Eq. 3.10) 
Table 3.11 reports p-values for both Patell (1976) parametric test-statistics and 
Corrado (1989) non-parametric test-statistics for comparison. Event studies with 
common event days or cross-sectional dependence present a potential upward bias in 
test-statistics and increased Type I errors. However, Brown and Warner (1985) find no 
significant bias in abnormal returns adjusted with a common benchmark, even in the 
presence of cross-sectional dependence. Small sample size also potentially results in 
misspecified test-statistics for non-normal distributions. The Corrado (1989) non-
parametric rank test makes no distributional assumptions and also provides well-
specified test-statistics for small samples. Campbell and Wasley (1993) recommend the 
Corrado (1989) rank test for event studies with potentially non-normal distributions. 
 116 
Table 3.11 CAARs for industry groups 
 
Notes: Table 3.11 reports average daily CAARs and associated test-statistics for the indicated event windows and 
different Fama-French 48 industry groupings. The best-performing Democratic and Republican industries are the top 
16 for each party with the greatest risk-adjusted return differences for the respective political parties from Table 3.4. 
The remaining 16 industries comprise a neutral portfolio. Industries grouped by contributions are the top 16 for 
Democrats and Republicans receiving the highest political contributions. The Wall Street analysts’ portfolio 
comprises the 16 industries analysts expect to benefit most under a Republican presidency. Bold indicates statistically 
significant p-values at 10 percent or greater. Lastly, the bottom of the table reports cumulative CRSP market returns. 
The event study analysis simply aims to measure how investor expectations for the 
outperformance of certain industries relate to election outcomes. Each event study 
portfolios comprise those industries with an expected common sensitivity to election 
cycles. Portfolio outperformance is the measure of returns in excess of expected returns 
predicted by the market model. In contrast, earlier analysis examines the marginal 
difference in realized industry performance conditional on whether a Democrat or 
Republican was president. 
(-20,0) (-10,0) (-5,0) (-2,0) (-1,0) (0,0) (0,1) (0,2) (0,5) (0,10) (0,20)
Cummulative Average Abnormal Returns (CAARS):
Best Performing 16 Industries - Democrats
CAARs 0.80% -0.07% 0.08% 0.24% 0.33% 0.15% -0.23% -0.16% -0.44% -0.27% 0.02%
Patell p-values 0.17 0.95 0.31 0.57 0.32 0.78 0.06 0.31 0.21 0.35 0.83
Rank test p-values 0.22 0.95 0.20 0.47 0.26 0.63 0.09 0.18 0.07 0.27 0.96
Best Performing 16 Industries - Republicans
CAARs -0.27% 0.29% 0.29% 0.26% 0.22% 0.36% 0.62% 0.45% 1.31% 0.60% 1.15%
Patell p-values 0.69 0.95 0.74 0.67 0.71 0.21 0.16 0.50 0.04 0.96 0.53
Rank test p-values 0.44 0.85 0.56 0.50 0.67 0.24 0.21 0.43 0.63 0.12 0.62
Neutral 16 Industries 
CAAR -0.54% -0.22% -0.37% -0.50% -0.56% -0.51% -0.39% -0.29% -0.87% -0.34% -1.17%
Patell p-values 0.38 0.27 0.19 0.83 0.73 0.09 0.47 0.94 0.24 0.70 0.90
Rank test p-values 0.24 0.45 0.19 0.96 0.82 0.08 0.24 0.61 0.09 0.03 0.14
Top 16 Industries by Contributions - Democrats
CAARs 0.87% 0.52% 0.82% 0.37% 0.16% 0.17% 0.38% 0.45% 1.12% 0.65% 1.20%
Patell p-values 0.01 0.13 0.02 0.30 0.79 0.74 0.53 0.47 0.24 0.83 0.43
Rank test p-values 0.03 0.29 0.07 0.27 0.77 0.92 0.89 0.80 0.49 0.18 0.76
Top 16 Industries by Contributions - Republicans
CAARs 0.64% 0.48% 0.60% 0.29% 0.13% 0.19% 0.34% 0.40% 0.35% -0.16% 0.13%
Patell p-values 0.02 0.13 0.04 0.47 0.91 0.77 0.70 0.56 0.44 0.09 0.50
Rank test p-values 0.02 0.16 0.03 0.35 0.82 0.95 0.91 0.99 0.14 0.04 0.37
Wall Street Analysts Top 16 Industries - Republicans
CAARs -1.09% -0.46% -0.87% -0.09% 0.01% 0.26% 0.18% 0.22% 0.15% -0.65% -0.82%
Patell p-values 0.86 0.95 0.58 0.87 0.96 0.31 0.65 0.40 0.24 0.27 0.54
Rank test p-values 0.54 0.90 0.94 0.85 0.97 0.55 0.75 0.59 0.72 0.02 0.18
Cummulative Market Returns:
CRSP Value Weight 2.02% 5.08% 3.03% 2.60% 2.60% 1.43% 1.81% 1.69% 2.69% 3.65% 4.88%
CRSP Equal Weight 1.90% 4.13% 2.19% 1.82% 1.79% 0.77% 1.24% 1.13% 2.96% 4.64% 7.84%
Event Window - 03 November 2004 Presidential election
 117 
To begin, Figure 3.5 illustrates and Table 3.11 details cumulative abnormal returns 
around event day zero for the best-performing Democratic and Republican industries. 
Republican industries (0.36%) marginally outperform Democratic industries (0.15%) on 
the event day. Republican industry CAARs steadily increase and then peak five days 
after the election at 1.31 percent. Only Republican industries increase over the first five 
trading days. Prior to the election, Democratic industry performance reflects exit polls 
predicting a Republican defeat. However, after an initial increase, outperformance peaks 
on the event day with announcement of a Republican victory. Subsequently, Democratic 
industry performance steadily declines with a -0.44 percent five-day CAAR. Neutral 
industries respond negatively to the Republican victory with a -0.51 percent abnormal 
event day return. Similar to Democratic industries, neutral industries subsequently track 
downward with a -0.87 percent five-day CAAR. Visually, Republican industry CAARs 
reflect the greatest differences in expected returns following the Republican victory. As 
Table 3.11 also reports, portfolio abnormal performance is, for the most part, 
statistically indistinguishable from zero. Except in one instance, Republican industries 
have highly insignificant CAARs. In total, the three industry groupings have 10 percent 
statistically significant Patell or rank test p-values only 12 percent of the time. Put 
differently, statistically significant outperformance occurs only slightly more than 
expected by random chance. The results suggest that CAARs for Democratic, 
Republican, and neutral industries alike primarily reflect noise. While the market 
initially responds to election news, insignificant differences in expected returns support 
market efficiency, considering the absence of excess industry performance over past 
political cycles. 
  
 118 
Figure 3.5 CAARs based on past industry performance 
 
Notes: Figure 3.5 illustrates cumulative average abnormal returns (CAAR) over 41 trading days centered on the 2004 
Bush-Kerry U.S. presidential election. The Democratic and Republican portfolios comprise of 16/48 Fama-French 
industries for each party that provide the greatest performance differences (DIF) reported in Table 3.4. Additionally, 
a neutral portfolio comprises the remaining 16/48 Fama-French industries. Table 3.10 details portfolio constituent 
industries. 
The analysis also examines abnormal returns for industries grouped by political 
campaign contributions and analysts’ recommendations. Industry contributions 
potentially indicate financial benefit and differences in expected performance. For 
instance, the oil and gas industries were top contributors to the 2004 Republican 
campaign. If presidential campaign promises translate into industry benefit, a 
Republican victory should increase expected returns for the oil and gas industries. 
Similarly, a Democratic victory should increase expected returns for top contributing 
Democratic industries. The top Republican contributing industries do generate 
significant 0.64 percent excess returns over 20 days prior to the election. However, the 
top Democratic contributing industries also provide significant 0.87 percent excess 
returns over the same period. Notably, most significant outperformance, for both the 
Democratic and Republican portfolios, occurs over five days prior to the election. The 
results perhaps indicate increased market uncertainty prior to the closely contested 
election, especially for industries with performance closely linked to the election 
outcome. For instance, Li and Born (2006) document that political uncertainty prior to 
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
-20 -15 -10 -5 0 5 10 15 20
CAARs
Event Day
Cummulative Average Abnormal Returns
Democrat Industries
Republican Industries
Neutral Industries
 119 
elections drives market performance. Additionally, Figure 3.6 illustrates similar event 
day performance again for both the top contributing Democratic (0.17%) and 
Republican (0.19 %) industries. Over the post-election event window, Democratic 
industries (1.20%) actually outperform Republican industries (0.13%), although excess 
returns are statistically insignificant. In general, excess returns for industries grouped by 
political contribution lack statistical significance. 
Lastly, the analysis documents the performance of industries Wall Street analysts 
pick to benefit from Republican victory, based on presidential campaign rhetoric. The 
oil extraction and investment industries feature prominently in analysts’ 
recommendations. Interestingly, after a 0.26 percent event day gain, industries selected 
by Wall Street analysts have a statistically significant and negative 0.65 percent 10-day 
CAAR, generating the greatest underperformance of all other industry groups. With that 
exception, all remaining CAARs for the Wall Street analyst selected industries lack 
statistical significance. Perhaps as Li and Born (2006) conjecture, the market takes a 
“wait and see” attitude before translating campaign promises and election outcomes into 
differences in expected returns.  
 120 
Figure 3.6 CAARs based on analyst recommendations and contributions 
 
Notes: Figure 3.6 illustrates cumulative average abnormal returns (CAARs) over 41 trading days centered on the 
2004 Bush-Kerry U.S. presidential election. The Democratic and Republican portfolios comprise the 16/48 Fama-
French industries for each party with the highest total industry campaign contributions, to either the political party or 
the candidate. Lastly, the analyst portfolio comprises the 16/48 Fama-French industries Wall Street analysts forecast 
to benefit most under a Republican presidency. Table 3.10 details portfolio constituent industries. 
Figure 3.7 illustrates Center for Research on Security Prices (CRSP) equal-weight 
and value-weight market cumulative returns. The analysis uses an expanded 120-
trading-day window to view long-term market direction. The general market has an 
upward trend over the entire window. Market performance noticeably increases 10 days 
before and after the election. Cumulative returns 10 days after the election are 3.65 
percent and 4.64 percent for the value-weighted and equal-weighted CRSP market 
indices. Similarly, Snowberg, Wolfers, and Zitzewitz (2007) document 2 to 3 percent 
market gains following election of a Republican president. Riley and Luksetich (1980) 
also document that the market prefers Republicans, at least in the short term. Generally, 
the 2004 presidential election has little noticeable effect on expected market 
performance. Given historical performance under Republicans, expected market returns 
should decline in response to a Republican victory. Market performance does decline 
but not until 40 trading days after the election. However, a steadily upward trending 
market prior to the election creates difficulty disentangling political effects from 
unrelated long-term market developments. 
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
-20 -15 -10 -5 0 5 10 15 20
CAARs
Event Day
Cummulative Average Abnormal Returns
Contributions - Democrats
Contributions - Republicans
WSJ Analysts - Republicans
 121 
Market reaction to the 2004 election suggests investors do not price a president’s 
political affiliation or election outcomes into industry returns. Industries that previously 
performed well under Republicans initially outperform other industries – but only 
insignificantly so. Industry political campaign contributions and Wall Street analysts’ 
projections also provide no additional insight into differences in expected returns 
surrounding presidential elections. Statistically insignificant excess returns, for all 
industries, suggest presidential election outcomes provide only a noisy signal of future 
industry performance. Analyzing only one presidential election limits the inferences 
drawn. However, the closely contested and politically divergent 2004 election offers a 
unique opportunity for an event study. If political cycles drive expected industry 
performance, the 2004 election should provide evidence. If not, other elections, with 
prior outcome certainty and convergent political agendas, would likely produce weaker 
results. 
Figure 3.7 Cumulative CRSP index returns for the 2004 presidential election 
 
Notes: Figure 3.7 illustrates cumulative value-weight and equal-weight CRSP market index returns over 121 trading 
days centered on the 2004 Bush-Kerry U.S. presidential election. 
  
-13.0%
-8.0%
-3.0%
2.0%
7.0%
12.0%
-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60
Event Days
Cummulative CRSP Index Returns
CRSP Value-Weighted Index
CRSP Equal-Weighted Index
 122 
3.7 Conclusion 
This study investigates industry returns for evidence of political cycles. Political 
industry cycles comprise both unexpected and expected elements (Santa-Clara and 
Valkanov (2003)). Unexpected industry returns occur over time, when Democratic or 
Republican policies positively or negatively surprise investors. Alternatively, 
unexpected returns can result from investor inattention to previous political cycles. 
Differences in expected industry performance reflect the market’s immediate response 
to the election of a Democratic or Republican president. Political risk premiums or 
changes in expected industry cash flows can cause differences in expected returns. The 
literature documents two political stock market cycles: presidential cycle and 
quadrennial cycle. A president’s political affiliation determines presidential cycles. The 
year of a four-year presidential term determines quadrennial cycles. 
As a first step, the study seeks to replicate the findings of prior studies documenting 
political cycles in the general market. Similar to Santa-Clara and Valkanov (2003), 
results confirm that the stock market performs best under Democratic presidencies. 
Over the sample of 21 presidencies from 1926 to 2006, stock returns are 8.6 percent 
higher under Democratic relative to Republican administrations. Santa-Clara and 
Valkanov (2003) also document higher market returns in the latter half of any 
presidential term. Similarly, results show that industry returns are 10 percent higher in 
the second half of all presidential terms. The observation of political stock market 
cycles violates market efficiency, potentially explained by industry analysis. 
The principal focus of the study is an examination of industry returns for evidence of 
political cycles. Industry cash flows provide a mechanism for political cycles to directly 
impact on stock prices. In turn, dominant industries, with cash flows sensitive to 
political agendas, potentially drive market-wide political cycles. Similar to the market, 
unadjusted industry returns are higher under Democrats and the last two years of any 
presidency. However, after a correction for common risk factors with a Fama-French 
three-factor model, industry performance exhibits no evidence of systematic or 
persistent political cycles. 
Market reaction to the 2004 election also reflects the absence of differences in 
expected industry performance in response to election outcomes. The event study 
 123 
analysis groups industries by political orientation based on past performance, campaign 
contributions, and analysts’ recommendations. Regardless of the industry grouping, the 
results document no evidence of statistically significant abnormal returns surrounding 
the closely contested and politically divergent 2004 presidential election. Industries that 
previously performed well under Republicans do exhibit the greatest response to the 
election result, although insignificantly so. While the market responds to election 
resolution, lack of statistically significant excess returns suggests that investors take 
election outcomes as a noisy signal of expected industry performance. 
Overall, the results question the popular belief that political cycles drive industry 
performance and provide evidence that political cycles are solely a market-wide 
phenomenon. Macroeconomic determinants may resolve the puzzling feature in the data 
of political stock market cycles. Possibly, investors formulate expectations for general 
stock market performance based on how a president’s political affiliation or 
opportunistic motivation influences macroeconomic determinants. Alternatively, as 
Santa-Clara and Valkanov (2003) suggest, asset returns may determine political 
outcomes, rather than the opposite. 
  
 124 
Chapter 4  Sector Rotation across the Business Cycle 
4.1 Introduction 
Sector rotation refers to a common investment strategy that targets investments in 
particular economic sectors at different stages of the business cycle. Bodie, Kane, and 
Marcus (2009) suggest the “way that many [financial] analysts think about the 
relationship between industry analysis and the business cycle is the notion of sector 
rotation.” Similarly, Lofthouse (2001) states that financial analysts “think in terms of 
stylized economic cycles, with different sectors performing at different stages of the 
cycle.” Fabozzi (2007, pg. 581) acknowledges, “Sector rotation strategies have long 
played a key role in equity portfolio management.” Validating its importance, the 
Chartered Financial Analyst (CFA) curriculum includes sector rotation in the “Core 
Body of Knowledge” essential for investment professionals.65 The number of websites 
dedicated to the topic lends further support to the popularity of sector rotation. For 
instance, an Internet search of “sector rotation” returns about 415,000 hits on Google.66 
Although it is difficult to ascertain the exact number of sector rotation investors, the 
recent growth in sector and industry financial instruments suggests the prominence of 
sector rotation with finance practitioners.67 In 2009, sector exchange-traded funds 
(ETFs) were the fastest growing segment of equity ETFs, with net inflows of U.S. $14 
billion and net asset value of U.S. $82 billion.68 Indicating the popularity of sector and 
industry funds, the Fidelity Select Sector Group offers a selection of 40 funds69 and 
iShares offers 60 different sector/industry ETFs.70 
Popular belief holds that certain sectors provide systematic business-cycle 
performance and that sector rotation generates excess market returns. According to 
Fidelity, technology stocks outperform the market following a business-cycle trough.71 
Just after a peak, investors are better off putting their money in utilities. Other financial 
                                                 
65 CFA Institute (2009) 
66 Google search conducted on 08 January 2011 
67 Popular funds include the Rydex/SGI All-Cap Opportunity H (RYSRX), Rydex/SGI All-Cap Opportunity 
(RYAMX), Claymore/Zacks Sector Rotation (XRO), and PowerShares Value Line Industry Rotation (PYH). 
68 Investment Company Institute (2010) 
69 http://personal.fidelity.com/products/funds/content/sector/products.shtml 
70 http://us.ishares.com/portfolio_strategies/investment_strategies/sector_strategies.htm 
71 http://personal.fidelity.com/products/funds/content/sector/cycle.shtml 
 125 
websites share Fidelity’s view on sector performance. Standard & Poor’s Guide to 
Sector Investing recommends technology after a recession.72 With the onset of an 
economic slowdown, Goldman Sachs73 and CNN Money74 advise investors to target 
utilities. Even though, as some suggest, “if you are in the right sector at the right time, 
you can make a lot of money very fast,” translating popular beliefs into a profitable 
sector rotation strategy presents a challenge.75 Sector rotation additionally requires 
correctly timing contemporaneous stages of the business cycle. Difficulty forecasting 
business-cycle stages might explain the lack of academic research on whether 
systematic sector performance provides an opportunity for profitable sector rotation. 
This study addresses that void in the literature. 
This study tests the two fundamental assumptions of sector rotation. Do certain 
sectors provide systematic performance across business cycles? Does sector rotation 
generate excess market performance? Bodie, Kane, and Marcus (2009) comment that 
“sector rotation, like any other form of market timing, will be successful only if one 
anticipates the next stage of the business cycle better than other investors.” This study 
overcomes the obstacle of correctly timing business cycles with a simple and intuitive 
approach. That approach gives sector rotation investors the benefit of the doubt, by 
assuming investors can perfectly time business cycle turning points. If the business 
cycle drives sector returns, then an investor who perfectly times business cycle stages 
and rotates sectors following popular belief on sector performance should generate 
excess returns. The base-case analysis begins with the assumption of a sector rotation 
strategy that follows conventional guidance on sector performance, following a popular 
sector rotation variant prescribed by investment analysts and practitioner guides. There 
are, however, many potential variants of sector rotation. Consequently, the analysis 
relaxes any assumption of a specific sector rotation model – testing the performance of 
all sectors across all business-cycle stages. Lastly, the study concludes with the analysis 
of an alternative sector rotation strategy. 
                                                 
72 Stovall (1996). 
73http://www.reuters.com/article/2008/01/09/us-usa-economy-goldman-idUSN0956579820080109 
74 Money (2006) http://money.cnn.com/2006/08/08/markets/fed_pause_stocks.moneymag/index.htm 
75 Money (2006) http://money.cnn.com/2006/08/08/markets/fed_pause_stocks.moneymag/index.htm 
 126 
Investors can choose to implement sector rotation at sector, industry, or firm level. 
The choice depends on how precisely an investor wants to target expected sector 
performance and the desired level of diversification. Sectors describe broad economic 
segments. Targeting sectors provides broad exposure with maximum diversification 
benefit. A common approach to sector rotation is industry-level implementation. 
Industries are sub-units of sectors. For instance, the Global Industrial Classification 
Scheme (GICS) details 10 sectors, 24 major industry groups, and 67 industries. 
Industries allow a more targeted approach to sector exposure, while still maintaining the 
benefit of diversification. For instance, the healthcare sector includes pharmaceutical, 
healthcare provider, and medical equipment sub-sectors or industries. A sector rotation 
investor might overweight pharmaceuticals relative to other healthcare sub-sectors, 
based on a specific view of expected industry performance. Lastly, stock-level 
implementation provides the most targeted approach, but lacks any diversification 
benefit. Industry-level analysis provides a detailed view of sub-sector groups that 
contribute to sector performance. Moreover, as evident by Fidelity Select, iShare 
offerings, and practitioner guides,76 industry-level implementation represents a common 
approach for sector rotation investors. The base-case analysis focuses on the Fama and 
French 49 industry portfolios. Expanded robustness analysis further considers 
alternative sector and industry groupings. 
The base-case analysis documents sector rotation outperformance – but only 
marginally so. The analysis investigates industry performance over 10 National Bureau 
of Economic Research (NBER) dated business cycles from 1948 to 2007. The NBER 
defines only broad phases of economic expansion and recession. As common practice, 
the analysis first divides broad NBER phases into additional sub-periods. The analysis 
then maps industries to business-cycle stages where popular belief anticipates optimal 
performance will occur. With few exceptions, industries expected to perform well in 
various stages show no systematic performance. The analysis next combines industries 
across stages to analyze whether conventional sector rotation generates outperformance. 
Investors, guided by popular belief on sector performance and with perfect foresight in 
timing business-cycle stages, achieve a risk-adjusted 2.3 percent annual return, before 
                                                 
76 See, for example, industry-level analysis in “Standard & Poor’s Guide to Sector Investing,” Stovall (1996). 
 127 
any transaction costs. To put that performance in perspective, investors who perfectly 
time business cycles can achieve higher returns simply by exiting equities when the 
economy enters a recession.77 With transaction costs, sector rotation performance 
quickly diminishes. The base-case analysis shows that if sector rotation investors earn 
excess returns, strategy performance has little to do with the popularly perceived driver 
– systematic sector performance across business cycles. 
The results are robust to a variety of tests and specifications. The analysis 
investigates whether the results differ when investors anticipate business-cycle stages 
early or late. Implementing sector rotation two months in advance drops performance 
from 2.3 percent to 1 percent. Outperformance drops to 1.8 percent when investors 
delay sector rotation by two months. Alternatively, the analysis examines business-cycle 
stages delineated by the Chicago Federal Reserve National Activity Index (CFNAI). 
The analysis also uses business-cycle proxies found in the literature to forecast industry 
performance. Whether the analysis investigates CFNAI stages or business-cycle 
proxies, the results hold. When considering alternative sector and industry groupings, 
the results remain unaffected. The main results are also robust to various performance 
measures such as the Sharpe ratio, Jensen’s alpha, and the recently introduced 
manipulation-proof performance measure (MPPM).78 The analysis further investigates 
whether the results depend on the risk-adjustment, as Chordia and Shivakumar (2002) 
and Avramov and Chordia (2006) document time-variant risk premiums conditional on 
business-cycle fluctuations. However, the results remain the same whether measured by 
a single-index, Fama and French three-factor, or Carhart four-factor model. 
  
                                                 
77 For instance, a simple market timing strategy that invests continuously in the market except during early recession 
generates 2.5 percent outperformance, compared with 2.3 percent for sector rotation. 
78 The MPPM controls for potentially biased performance measures due to non-iid returns. See Goetzmann, Ingersoll, 
Spiegel, and Welch (2007) for a complete discussion. 
 128 
Figure 4.1 Popular guidance on sector rotation 
 
Notes: Figure 4.1 illustrates alternative sector rotation strategies. 
Lastly, the study generalizes the base-case analysis to allow for all variations of 
sector rotation. The base-case analysis follows a commonly accepted version of sector 
rotation, as defined by Stovall (1996) in Table 4.1 and illustrated by Standard & Poor’s 
in Figure 4.1. However, there are other potential variants of sector rotation. The results 
are thus subject to the criticism of being limited to a specific sector rotation model, 
among any number of other potential sector rotation models. To counter such criticism, 
the analysis tests for systematic performance of any sector across any business-cycle 
stage. Measuring statistically significant outperformance, the generalized results align 
with a hypothesis of neither systematic nor persistent differences in sector returns across 
 
http://personal.fidelity.com/products/funds/content/sector/cycle.shtml 
 
http://www2.standardandpoors.com/spf/pdf/index/Global_Sector_Investing.pdf 
 129 
business-cycle stages. The significance levels observed are only marginally different 
from those expected to occur randomly, without any systematic outperformance. The 
result suggests that no sector rotation variant provides systematic outperformance, 
questioning the popular belief that timing sector investments with business cycles 
generates excess market performance. 
Table 4.1 Business cycle stages of expected industry performance 
 
Notes: Table 4.1 reports the business cycle stage of anticipated sector/industry outperformance following the Stovall 
(1996) classification and the investment websites illustrated in Figure 4.1. The table divides the periods of expansion 
into three equal stages (early/middle/late) and periods of recession into two equal stages (early/late). The Fama and 
French 49 industry portfolios (excluding “other”) map to a corresponding sector for the base-case analysis. 
This study contributes to the literature as the first to question the underlying 
assumptions of sector rotation: systematic sector performance and the opportunity for 
investors to profitably time sector rotation with the business cycle. Recent papers, such 
as Elton, Gruber, and Blake (2009) and Avramov and Wermers (2006), suggest the 
importance that sector rotation plays in mutual fund performance. Apart from a return 
predictability perspective, this study provides additional insights. Sector rotation 
generates order flows, which transmit information about asset fundamentals. For 
instance, Beber, Brandt, and Kavajecz (2010) provide evidence that sector-order flows 
forecast macroeconomic conditions. The evidence suggests that sector-order flows, 
however, do not translate into systematic sector performance. Evidence in Avramov and 
Wermers (2006) finds that switching industry investments across business cycles drives 
equity fund performance. Jiang, Yao, and Yu (2007), similarly, conclude that industry 
rotation underlies mutual fund timing strategies, where fund managers switch between 
Early Expansion - Stage I Middle Expansion - Stage II Late Expansion - Stage III Early Recession - Stage IV Late Recession - Stage V
Technology: Basic Materials: Consumer Staples: Utilities: Consumer Cyclical:
Computer Software Precious Metals Agriculture Gas & Electrical Utilities Apparel
Measuring & Control Equip. Chemicals Beer & Liquor Telecom Automobiles & Trucks
Computers Steel Works Etc Candy & Soda Business Supplies
Electronic Equipment Non-Metallic & Metal Mining Food Products Construction
Transportation: Capital Goods: Healthcare Construction Materials
General Transportation Fabricated Products Medical Equipment Consumer Goods
Shipping Containers Defense Pharmaceutical Products Entertainment
Machinery Tobacco Products Printing & Publishing
Ships &  Railroad Equip. Energy: Recreation
Aircraft Coal Restaurants, Hotels, Motels
Electrical Equipment Petroleum & Natural Gas Retail
Services: Rubber & Plastic Products
Business Services Textiles
Personal Services Wholesale
Financial:
Banking
Insurance
Real Estate
Trading
Period of  Expansion Period of  Recession
 130 
cyclical and non-cyclical stocks. A natural question to ask is whether mutual funds 
follow conventional sector rotation or alternative timing strategies. The results suggest 
that mutual funds profit from the latter. This study contributes to a renewed interest in 
the literature on rotation strategies and industry allocation, providing additional insight 
into these questions, among others. 
4.2 Literature Review and Hypotheses Development 
One can dismiss, within the framework of rational expectations and the efficient market 
hypothesis, the idea that investors systematically profit from sector rotation. Sector 
prices should instantaneously reflect all available information and fundamental value – 
irrespective of business-cycle stages. Yet, the prominence of sector rotation in practice 
suggests that investors profit from timing systematic sector performance with the 
business cycle. 
The apparent ability to profit from sector rotation might be consistent with the Hong 
and Stein (1999) gradual information diffusion hypothesis. Gradual information 
diffusion, as Hong and Stein (1999) describe, involves two groups of traders (news 
watchers and arbitrageurs) and the lead-lag relation of their responses to economic 
news. News watchers have a limited ability to process news and consequently revise 
asset prices with a delay. Arbitrageurs, in contrast, fully incorporate news in their price 
adjustments and devise simple trading strategies that generate excess returns. 
Analogously, one can view sector rotation investors as arbitrage traders who respond to 
economic news by profitably timing sector rotation. 
Hong, Torous, and Valkanov (2007) empirically test the gradual information 
diffusion hypothesis with U.S. industries. They observe an informational delay between 
general stock market returns and the returns of industries with close economic links. 
Their study covers the period 1946–2002 with Fama and French industry data. Returns 
for industries with strong economic links (e.g. metals, services, and petroleum) lead the 
general market by up to two months. Hong, Torous, and Valkanov (2007) conjecture 
that economic news affects industry fundamentals differently and that the informational 
content in the performance of certain industries diffuses slowly across asset markets. 
 131 
Extending Hong, Torous, and Valkanov (2007), a study by Menzly and Ozbas (2010) 
provides evidence of industry return continuation, which they attribute to the sequential 
diffusion of information across related industries. Menzly and Ozbas (2010) observe 
that certain industries transmit price-sensitive information with a delay to related 
upstream and downstream firms. Parallels exist between the findings of Menzly and 
Ozbas (2010) and popularly anticipated industry performance across business cycles. 
For instance, sector rotation investors anticipate early expansion performance in basic 
materials that leads to middle expansion performance in manufacturing. Menzly and 
Ozbas (2010) observe that investors who trade on the knowledge of industry return 
momentum earn 8.7 percent annual outperformance. Similarly, this study examines 
whether sequential sector performance across the business cycle provides an 
opportunity for profitable sector rotation. 
Related literature documents differences in the informational content of economic 
news, dependent on business-cycle conditions. McQueen and Roley (1993), using data 
from 1977 to 1988, show differences in the effect of economic news on Standard & 
Poor’s 500 performance. McQueen and Roley (1993) find that the S&P 500 decreases in 
value with news of economic growth when the economy is strong and increases in value 
when the economy is weak. Boyd, Hu, and Jagannathan (2005) examine the effect of 
unemployment news on returns to the S&P 500 from 1957 to 2000. Boyd, Hu, and 
Jagannathan (2005) find that the impact of unemployment news on equity returns 
depends on whether the economy is in a period of expansion or recession. The empirical 
evidence thus shows that the effect of economic news on expected sector performance 
depends not only on the sector but also on current business-cycle conditions. 
While earlier research typically examines asset performance across broad phases of 
economic expansion and contraction, DeStefano (2004) and Guidolin and Timmermann 
(2008), among others, observe finer business-cycle partitions. Guidolin and 
Timmermann (2008) delineate four different stages of the economy characterized by 
market volatility. Using monthly U.S. stock market data from 1927 to 2005, Guidolin 
and Timmermann (2008) find that economic stages provide information for rebalancing 
portfolios formed on firm capitalization and value dimensions. 
 132 
Empirical research provides evidence that fund managers time their sector 
investments with business cycles and that their order flows coincide with conventional 
sector rotation. Lynch, Wachter, and Boudry (2004) also note that fund manager 
performance varies over business cycles. Avramov and Wermers (2006) show that 
predictable variation in fund performance relates to a manager’s skill in timing industry 
rotation with NBER business-cycle turning points. Jiang, Yao, and Yu (2007) also 
observe that fund managers adjust industry allocations based on common business cycle 
proxies. In a related study, Beber, Brandt, and Kavajecz (2010) conclude that active 
order flows, defined as flows in excess of market capitalization, directly link to 
economic news. Notably, for the motivation of this study, Beber, Brandt, and Kavajecz 
(2010) observe that aggregate sector rebalancing emulates a conventional sector rotation 
strategy, one that exploits the relative outperformance of certain sectors at different 
business-cycle stages. Moreover, and of further interest for this study, Beber, Brandt, 
and Kavajecz (2010) find institutional order flows into certain sectors predict economic 
direction. For instance, order flows into the basic materials sector predict economic 
expansion while order flows into the telecommunication, consumer discretionary, and 
financial sectors predict economic contraction. Such investment flows also coincide 
with popular belief on the sequence of sector performance. 
One can view the systematic recurrence of sector performance, such as the financial 
sector in early contraction, as a cyclical variant of price continuation consistent with 
gradual information diffusion. If so, under-reaction to economic news might possibly 
explain short-term and medium-term price continuation in sector returns, resulting in 
long-term overreaction and subsequent price reversal. Perhaps not coincidentally, and of 
interest to the basic premise of sector rotation, the frequency of price continuation and 
price reversal observed in empirical studies roughly corresponds with business-cycle 
stages and complete business cycles. 
Interestingly, the literature documents that industry performance largely explains 
price momentum. In an influential study, Moskowitz and Grinblatt (1999) examine 
industry return momentum from 1963 to 1995, concluding that industry affiliation 
drives stock momentum. Moskowitz and Grinblatt (1999) attribute return momentum to 
cross-industry gradual information diffusion. Moskowitz and Grinblatt (1999) speculate 
that investors herd in popular industries, similar in spirit to conventional sector rotation 
 133 
investors. O'Neal (2000) establishes the profitability of industry momentum strategies 
using Fidelity Sector Select Funds from 1989 to 1999. Forerunning later research by 
Beber, Brandt, and Kavajecz (2010), O'Neal (2000) also finds that industry return 
continuation coincides with business cycles. Bacmann, Dubois, and Isakov (2001), 
likewise, confirm a link between industry return continuation and business cycles. 
An empirical examination of cyclical sector performance is topical for both financial 
researchers and investors. According to Hong and Stein (1999), informed arbitrage 
traders can generate excess returns with simple trading strategies based on the release of 
economic news. Sector- and industry-level investing also constitutes a dynamic growth 
segment in financial markets. Cavaglia, Brightman, and Aked (2000) and Conover, 
Jensen, Johnson, and Mercer (2008) document the increased importance of industry-
level versus country-level investing. Kacperczyk, Sialm, and Zheng (2005), in a study 
of U.S. mutual funds from 1984 to 1999, find that active managers with concentrated 
industry positions generate the greatest outperformance. From a practitioner’s 
perspective, the widespread availability of sector funds and ETFs makes sector 
allocation strategies more feasible than ever. Nonetheless, there is an apparent absence 
of empirical research on sector performance over business cycles. 
Related literature does describe the performance of alternative business-cycle timing 
strategies. For instance, Siegel (1991) illustrates the potential of profitably timing 
allocations between equities and cash. Over an extended 1802–1990 sample, Siegel 
(1991) documents 12 percent annual market outperformance switching between equity 
and cash at NBER business-cycle turning points. Brocato and Steed (1998) similarly 
observe market outperformance rebalancing portfolios at NBER turning points. Further, 
Levis and Liodakis (1999) and Ahmed, Lockwood, and Nanda (2002) report 
outperformance to rotation strategies based on firm characteristics (such as earnings, 
value, and capitalization) conditioned by well-known business-cycle variables. Research 
by Conover, Jensen, Johnson, and Mercer (2008) show 3.4 percent annual 
outperformance to a strategy that times investments in cyclical and non-cyclical stocks 
with Federal Reserve monetary policy. While closely related, this study fundamentally 
differs from previous research, by thoroughly analyzing sector and industry 
performance across different measures of business-cycle stages. Additionally, as Fama 
and French (1997) and Lochstoer (2009) identify time-variant industry-risk premiums 
 134 
related to business cycles, this study also evaluates industry performance using different 
risk correction measures. 
Based on the results, sector performance across business cycles is inconsistent with 
the gradual information diffusion hypothesis. Economic news appears to transmit 
immediately, rather than gradually, across sectors. Hou (2007) provides an explanation 
of why gradual information diffusion might not apply to sector performance. Examining 
U.S. stock market returns over 1963–2001, Hou (2007) describes slow information 
diffusion as primarily a within-industry effect, rather than a cross-industry effect. Hou 
(2007) finds information transmits sluggishly from industry leaders to industry 
followers. The results provide no evidence of systematic sector performance across 
business-cycle stages. As such, the results suggest that sector rotation investors have no 
opportunity to realize excess returns trading on systematic sector performance or 
gradual information diffusion. Unsystematic sector performance suggests markets are 
fully rational, rather than boundedly rational as gradual information diffusion 
prescribes. 
The above discussion leads to a formal statement of this study’s null and alternative 
hypotheses.  
H0: Industry returns are unrelated to the stage of a business cycle stage. 
H1: There is a systematic relationship between industry performance and stages of the 
business cycle. 
H2: Rotating sector investments with business cycle stages generates systematic excess 
returns. 
Answering these hypotheses tests the fundamental assumption of sector rotation 
investors that timing industry allocations with the business cycle is a profitable 
investment strategy. 
4.3 Business Cycles 
4.3.1 NBER business cycle dates 
The base-case analysis covers 10 business cycles from January 1948 to December 2007. 
Two considerations determine the starting point of the sample. First, the starting point 
 135 
eliminates the possibility of business-cycle distortions caused by the Great Depression 
or World War II.79 For instance, although the U.S. economy was officially in a recession 
during 1945, industries still operated at full wartime production. Secondly, studies such 
as Stock and Watson (2002) suggest business-cycle durations changed after World War 
II. Fama (1975) accredits the change to adoption of the 1951 Federal Reserve Accord. 
The Accord allows the Federal Reserve Bank to moderate business cycles through 
interest rate adjustments. 
The official U.S. Government agency responsible for dating business cycles is the 
NBER. While academics and practitioners widely accept NBER cycle reference dates, 
other business-cycle measures are also available.80 The NBER dates cycle peaks and 
cycle troughs that broadly define phases of economic expansion and economic 
recession. Panel A of Table 4.2 reports business cycle durations from business cycle 
peak to business cycle peak. The sample covers the 10 business cycles enumerated in 
the far left column of Panel A. Each business cycle spans the first month following a 
peak to the subsequent peak. Business cycles average 70 months over the sample. 
Earlier business cycle durations are much shorter than recent cycles, particularly during 
phases of economic expansion.81 
  
                                                 
79 See, for example, Investment Company Institute (2009) and Chatterjee (1999). 
80 For a survey of business cycle dating methodologies, see Cover and Pecorino (2005). 
81 Moore (1974) provides a detailed discussion of post-1948 differences in business cycle dynamics. 
 136 
 
Table 4.2 NBER reference business cycle dates and stage partitions 
 
Notes: Panel A of Table 4.2 reports NBER published business cycle peak and trough reference dates. Periods of 
recession run from the first month following a cycle peak to the subsequent trough, and periods of expansion run 
from the first month following a cycle trough to the subsequent peak. The sample covers 10 business cycles from 
1948 to 2007, enumerated in the first column. The last column reports the total months in a business cycle from one 
month after a peak to the next peak. The last recorded NBER business cycle date is the economic peak dated 
December 2007. Panel B of Table 4.2 reports the duration in months for stages of expansion and recession that 
correspond with the business cycles reported in Panel A. The analysis partitions NBER defined periods of expansion 
into three equal stages (early, middle, and late) and NBER defined periods of recession into two equal stages (early 
and late). The bottom of Panel B reports the average duration of each business cycle stage. 
4.3.2 Business cycle stages 
While the NBER defines broad economic phases, researchers and investment 
practitioners commonly divide expansions and recessions into more discrete stages. 
Investment professionals and practitioner guides, such as Stovall (1996), commonly 
divide expansions into three equal stages (early/middle/late) and recessions into two 
equal stages (early/late). Three stages of expansion allow for the longer duration of 
expansions relative to recessions. Other research, such as DeStefano (2004), divides 
both expansions and recessions into two equal stages. The base-case analysis evaluates 
sector/industry performance across five business cycle stages. Subsequent analysis 
Panel A: 
Business Peak Trough Peak Total
Cycle Date Date Date Months
1 11/48 10/49 07/53 56
2 07/53 05/54 08/57 49
3 08/57 04/58 04/60 32
4 04/60 02/61 12/69 116
5 12/69 11/70 11/73 47
6 11/73 03/75 01/80 74
7 01/80 07/80 07/81 18
8 07/81 11/82 07/90 108
9 07/90 03/91 03/01 128
10 03/01 11/01 12/07 81
Panel B:
Business
Early Stage Late Stage Total Early Stage Middle Stage Late Stage Total 
Cycle Months Months Months Months Months Months Months
1 6 5 11 15 15 15 45
2 5 5 10 13 13 13 39
3 4 4 8 8 8 8 24
4 5 5 10 35 35 36 106
5 6 5 11 12 12 12 36
6 8 8 16 19 19 20 58
7 3 3 6 4 4 4 12
8 8 8 16 30 31 31 92
9 4 4 8 40 40 40 120
10 4 4 8 25 25 23 73
stage average: 5 5 10 20 20 20 60
NBER business cycle dates from Jan 1948 - Dec 2007
 Number of months in NBER delineated business cycle stages
Periods of  ExpansionPeriods of  Recession
 137 
further evaluates performance across two-stage and four-stage business cycle partitions 
in subsequent robustness checks. 
Figure 4.2 Stylized business cycles with stage partitions 
 
Notes: Figure 4.2 illustrates a stylized business cycle. The official government agency responsible for dating U.S. 
business cycles is the National Bureau of Economic Research (NBER). The NBER publishes dates for business cycle 
peaks and troughs. Phases of expansion run from the month following a trough to the next peak and phases of 
recession run from the month following a peak to the next trough. Similar to Stovall (1996) and common practice, the 
analysis divides expansions into three equal stages (early/middle/late) and recessions into two stages (early/late). 
The analysis measures expansions from the first month following a cycle trough to 
the subsequent cycle peak and recessions from the first month following a cycle peak to 
the subsequent cycle trough. The analysis also delineates three equal stages of 
expansion and two equal stages of recession. The five business cycle stages are early 
expansion (Stage I), middle expansion (Stage II), late expansion (Stage III), early 
recession (Stage IV), and late recession (Stage V). Panel B of Table 4.2 reports the 
duration of expansions, recessions, and stages over 10 business cycles occurring from 
1948 to 2007. Recessions average approximately 10 months and expansions 
approximately five years. 
4.3.3 Evaluation of business cycles proxies 
The analysis first investigates whether the five NBER delineated stages are consistent 
with well-known business cycle proxies. The common business cycle proxies (BCP) in 
the literature are term-spread, default-spread, dividend yield, unemployment, and 
NBER trough NBER trough
NBER peak
Stage I Stage II Stage III Stage IV StageV
Expansion Recession
Early Expansion (Stage I) 202 months Early Recession (Stage IV) 53 months
Middle Expansion (Stage II) 201 months Late Recession (Stage V) 51 months
Late Expansion (Stage III) 213 months
Stages of Expansion Stages of Recession
 138 
industrial production. Studies by Keim and Stambaugh (1986), Chen, Roll, and Ross 
(1986), Fama and French (1989), Schwert (1990), Campbell (1987), Chen (1991), 
Jensen, Mercer, and Johnson (1996), and Petkova (2006), among others, document the 
relation between these proxies and business-cycle conditions. 
Panel A of Table 4.3 provides a summary of expected business cycle proxy changes 
over the five NBER delineated stages. For instance, term-spread, default-spread, and 
dividend yield are smallest near economic peaks and largest near economic troughs 
(Fama and French (1989)).82 The expectation is that these variables will decrease across 
early, middle, and late stages of expansion. Conversely, these same variables should 
increase across stages of early and late recession. Other studies, such as Balvers, 
Cosimano, and McDonald (1990) and Chen (1991), document a close link between 
business cycles and both unemployment rates and industrial production. Stock and 
Watson (1998) and Hamilton and Lin (1996) show, for example, that industrial 
production peaks and unemployment rates bottom out as the economy enters recession. 
Industrial production should increase across successive stages of expansion and 
decrease across successive stages of recession. Conversely, unemployment rates should 
decrease across early, middle, and late expansion, then increase across early and late 
recession. 
  
                                                 
82 The term-spread, default-spread, and dividend yield data come from http://www.globalfinancialdata.com 
 139 
Table 4.3 Business cycle proxies across business cycle stages 
 
Notes: Panel A of Table 4.3 lists the expected change in business cycle proxies from one business cycle stage to the 
next. Panel B of Table 4.3 reports business cycle proxy means by business cycle stage and changes in means from the 
preceding stage estimated with Equation 4.1, where business cycle dummy variables (Ds) take the value of one or zero depending on the current business cycle stage. The analysis then calculates the difference in proxy means 
between successive business cycle stages. As an example, Panel B reports an average 0.5% difference in term-spread 
between the stages of early expansion and late recession (1-5). Lastly, the analysis performs a simple difference in means test, to verify the statistical significance of the difference in means between the current and preceding stage. 
The table reports p-values under a null hypothesis of no difference in proxies across successive business cycle stages, 
formally stated as Ho: s=s-1. 
5
,
1
t s s t t
s
BCP D 

                 (Eq. 4.1) 
Panel B of Table 4.3 reports proxy averages by business-cycle stage estimated with 
Equation 4.1, where Ds is a dummy variable that takes the value of one or zero 
dependent on the current business cycle stage. Next, the table reports changes in 
business-cycle proxy values (s-s-1) between successive business-cycle stages. As an 
example, there is an average 0.5% difference in term-spread between early expansion 
and late recession. Panel B establishes that changes in the selected business-cycle 
variables track NBER delineated business-cycle stages and show the expected sign as 
reported in Panel A. For instance, the results should indicate a significantly negative 
term-spread difference between early expansion and late recession. The analysis tests 
for statistical significance using a simple difference in means test. Panel B reports 
p-values under the null hypothesis of no difference in business-cycle proxies across 
successive stages, formally stated as Ho: s = s-1. Failure to reject the null would 
indicate no statistically significant difference in the business-cycle proxy across 
successive stages and would invalidate the stage delineations. 
  
Panel A:
Term-spread
Default-spread
Dividend yield
Unemployment
Industrial production
Panel B:
Factor Mean Change p-value Mean Change p-value Mean Change p-value Mean Change p-value Mean Change p-value
Term-spread 0.020 0.005 0.00 0.013 -0.008 0.00 0.003 -0.010 0.00 0.006 0.003 0.05 0.015 0.009 0.00
Default-spread 0.010 -0.003 0.00 0.008 -0.002 0.00 0.008 0.000 0.22 0.010 0.002 0.00 0.013 0.003 0.01
Dividend yield 0.035 -0.010 0.00 0.032 -0.003 0.01 0.032 0.000 0.96 0.044 0.012 0.00 0.045 0.001 0.77
Unemployment 6.616 -0.497 0.04 5.329 -1.287 0.00 4.501 -0.828 0.00 5.670 1.168 0.00 7.114 1.444 0.00
Industrial production 3.992 10.472 0.00 6.020 2.028 0.00 4.233 -1.787 0.00 -1.375 -5.608 0.00 -6.480 -5.105 0.00
positive
negative
Change
Late Recession
positive
positive
positive
positive
Change
Early Recession
positive
positive
positive
positive
negative
Late Expansion
negative
negative
negative
negative
Middle Expansion
negative
negative
negative
negative
Early Expansion
negative
negative
negative
positive
Early Expansion Middle Expansion Late Expansion Early Recession Late Recession 
Stage I Stage II Stage III Stage IV Stage V
negative
positive
Change Change Change
 140 
 
The results document that changes in the business-cycle proxies across successive 
business-cycle stages, with few exceptions, have the expected sign and are highly 
significant. For instance, changes in unemployment rates from one business-cycle stage 
to the next are all significantly negative across stages of expansion and significantly 
positive across stages of contraction. Similarly, changes in default-spread are negative, 
as expected, during early and middle expansion, and positive during early and late 
recession. The change in dividend yield is insignificant in both late expansion and late 
recession, indicating that dividend yield provides a weak signal of business-cycle 
turning points. The change in industrial production growth is also as expected, except 
during late expansion. Overall, the results reported in Table 4.3 confirm that the NBER 
delineated stages are consistent with well-known business-cycle proxies and thus 
encapsulate progressive stages of business-cycle dynamics. 
4.4 Industry Performance across Business Cycles 
4.4.1 Data description 
For the base-case scenario, monthly market, industry, and Treasury bill return data 
come from the Kenneth French website. Market returns represent the total value 
weighted returns for all NYSE, AMEX, and NASDAQ listed stocks. The analysis 
initially uses the Fama and French 49 industry portfolios. Fama and French map firms 
to industry groupings based on their standard industrial classification (SIC).83 Firms 
mapped to the “other” industry come from a variety of sectors and industries. As such, 
the “other” industry holds no relevance in a sector rotation strategy. Consequently, the 
analysis omits the “other” industry, leaving 48 of the original Fama and French 49 
industries.84 The one-month Treasury bill serves as a proxy for the risk-free interest rate. 
The analysis reports all results as continuously compounded annualized returns. 
                                                 
83 See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html for further detail on the data and the 
formation of industry portfolios. 
84 The “other” industry group represents approximately 3.5 percent of total firms listed on NYSE, AMEX, and 
NASDAQ. 
 141 
4.4.2 Popular guidance on industry performance 
Table 4.1 shows the particular stage of the business cycle where popular belief 
anticipates industries will perform best. The base case follows the popular Stovall 
(1996) practitioner guide to sector investing. Stovall (1996) divides all equities into 10 
basic sectors. He then maps sectors and sub-sector industry groups to one of five 
business cycle stages.85 For example, Stovall suggests that the technology and 
transportation sectors provide early expansion performance, basic materials and capital 
goods provide middle expansion performance, and so forth. As Table 4.1 illustrates, 
there are four technology sub-sector industries and two transportation sub-sector 
industries. Conventional guidance suggests each industry in those sectors provides early 
recession performance. Performance then shifts from sector to sector across business-
cycle stages. The base-case analysis maps each of the 48 industry portfolios to a 
corresponding sector, then maps each sector to the business-cycle stage of anticipated 
sector performance, following the Stovall (1996) classification. 
4.4.3 Nominal industry performance 
Table 4.4 provides industry descriptive industry statistics and nominal performance for 
the business-cycle stage popular belief anticipates outperformance will occur.86 The 
table reports the average number of firms, number of observations, mean returns, 
standard deviation of returns, and single-index betas by the indicated stage. For 
comparison, Table 4.4 reports mean returns, standard deviation of returns, and single-
index betas for the full sample 1948–2007. The table also reports industry averages and 
market statistics beneath each business-cycle stage. 
The second column of Table 4.4 reports the average number of firms in an industry. 
Implementing sector rotation at industry level allows for more precise targeting of 
performance. The wide variety of available industry funds and ETFs reflects the 
popularity of industry-level investing. The increased precision targeting industry versus 
                                                 
85 Lofthouse (2001) traces a similar approach of mapping sectors to stylized stages of economic cycles back to 
Markese (1986). There are also different variants of mapping sector performance to business-cycle stages. Salsman 
(1997) uses dividend yield, short-term interest rates, and precious metal prices to map sector performance. The 
present study concludes with the total relaxation of any assumed sector rotation model. 
86 Full results for all Fama and French 49 industries across all business cycle stages are available upon request. 
 142 
sector performance, however, comes at the cost of reduced diversification benefits. The 
defense, tobacco, and coal industries, for instance, comprise on average fewer than 10 
firms. As such, investments in those industries are subject to a high level of firm-
specific risk. It is unlikely, however, that sector rotation investors would invest in only 
one industry during a particular business-cycle stage. For example, there are 12 
industries, including defense, expected to provide middle expansion performance. 
Overall, conventional sector rotation investors would thus hold a well-diversified 
middle expansion portfolio.87 
The base-case analysis initially measures nominal industry performance to determine 
whether significant differences occur over business cycles. The analysis then observes 
whether industry performance coincides with popular belief. Computer software, for 
instance, should provide early expansion performance and basic materials should 
provide middle expansion performance. Equation 4.2 estimates nominal industry 
performance for business-cycle stages. 
5
1
it is st it
s
r D 

                 (Eq. 4.2) 
The equation runs a regression of nominal industry returns (ri) on a dummy variable 
(Ds) that indicates one of five different business-cycle stages. As an example, D1 takes 
the value of one during months of early expansion and zero in all other months. Dummy 
variables D1, D2, and D3 correspond with the three stages of economic expansion 
(early/middle/late). Dummy variables D4 and D5 correspond with the two stages of 
economic recession (early/late). Thus, the is regression coefficients measures average 
nominal industry returns for each of five business-cycle stages. For comparison, the last 
three columns in Table 4.4 report industry statistics over the full sample period. Table 
4.4 also reports p-values from a Wald test under the null hypothesis that industry returns 
are not significantly different across business-cycle stages. However, in most cases, the 
p-values reject the null, indicating that industry performance varies across business-
cycle stages. Sector rotation investors would find this initial result encouraging. Failure 
                                                 
87 For an overview of tradeoffs in implementing sector rotation strategies at sector, industries, and firm levels, see 
http://us.ishares.com/portfolio_strategies/investment_strategies/sector_strategies.htm 
 143 
to reject the null hypothesis of equal returns would question the basic premise of sector 
rotation from the start. 
Table 4.4 also reports average market returns beneath each business-cycle stage. The 
analysis compares industry and market returns to provide a simple relative return 
metric. As an example, Table 4.4 reports transportation industry returns of 25 percent, 
compared with 17 percent market returns for early expansion. The transportation 
industry thus provides market outperformance, where conventional wisdom expects. 
However, the realization of expected outperformance does not always occur. Out of the 
48 industries, 34 have nominal returns higher than market returns, in the stage of 
expected outperformance. Thus, 70 percent of industries offer the expected higher 
nominal performance. Market outperformance, however, comes at a price. All but one 
industry (food products) has higher return volatility than the market. Observing average 
industry performance for two stages reveals surprising results. The 14 percent average 
return for industries expected to perform well in early expansion actually underperforms 
the market by 3 percent. Similarly, average returns for industries expected to perform 
well in middle expansion earn 1 percent less than the market. 
Table 4.4 Industry summary statistics by business-cycle stages 
 
no. no. Wald
Sectors/Industries firms obs. mean std.dev. beta p-value mean std.dev. beta
Early Expansion - Stage I:
Computers 90 201 0.13 0.22 1.38 0.00 0.13 0.24 1.24
Computer Software 175 130 0.00 0.34 1.71 0.15 0.02 0.42 1.79
Electronic Equipment 157 201 0.17 0.25 1.50 0.00 0.11 0.26 1.44
Measuring & Control 70 201 0.10 0.22 1.36 0.00 0.12 0.24 1.32
Shipping Containers 26 201 0.18 0.17 0.96 0.00 0.12 0.18 0.98
Transportation 93 201 0.25 0.17 1.02 0.00 0.10 0.19 1.09
Industry Averages 0.14 0.23 1.32 0.03 0.10 0.25 1.31
Market 201 0.17 0.13 1.00 0.00 0.12 0.14 1.00
Middle Expansion - Stage II:
Chemicals 79 202 0.12 0.17 1.12 0.00 0.11 0.17 1.01
Steel Works 78 202 0.13 0.22 1.23 0.00 0.10 0.23 1.25
Precious Metals 20 166 0.08 0.32 0.76 0.58 0.08 0.35 0.71
Mining 24 202 0.12 0.23 1.19 0.00 0.12 0.21 1.01
Fabricated Products 20 166 0.12 0.20 1.00 0.01 0.05 0.24 1.10
Machinery 149 202 0.17 0.18 1.22 0.00 0.11 0.19 1.17
Electrical Equipment 68 202 0.19 0.19 1.26 0.00 0.14 0.20 1.19
Aircraft 25 202 0.19 0.21 1.15 0.00 0.14 0.23 1.12
Shipbuilding &  Railroad 11 202 0.08 0.19 1.15 0.01 0.10 0.21 1.00
Defense 7 166 0.15 0.21 1.07 0.02 0.12 0.23 0.83
Personal Services 38 202 0.12 0.22 1.17 0.00 0.09 0.23 1.08
Business Services 166 202 0.13 0.16 1.04 0.00 0.10 0.18 1.09
Industry Averages 0.13 0.21 1.11 0.05 0.10 0.22 1.05
Market 202 0.14 0.13 1.00 0.00 0.12 0.14 1.00
Business Cycle Stage Full Sample 1948:01-2007:12 
 144 
Continued: 
 
Notes: Table 4.4 reports industry summary statistics for the business cycle stage popular belief anticipates 
outperformance will occur, as listed in Table 4.1. The table reports nominal mean returns and standard deviations 
(std.dev.) as annualized rates. Equation 4.2 estimates nominal mean industry returns for each business-cycle stage, 
with a regression of industry returns (ri) on the business-cycle stage dummy variables (Ds). The stage dummy variables take a value of one or zero at time t depending on the current business-cycle stage. The reported industry 
betas are from a single-index model. The table also reports Wald p-values under a null hypothesis of equal industry 
returns across all five business-cycle stages. For comparative purposes, Table 4.4 provides industry summary 
statistics for the full sample 1948–2007. The table reports equally weighted industry averages and market returns 
beneath each business-cycle stage. Column 1 reports the average number of firms (no. firms) included in an industry. 
Column 2 reports the number of industry return observations (no. obs.) included in a business-cycle stage. 
Based on the base-case results, popular belief holds true in the remaining three 
stages. Industries on average outperform the market, as expected, in late expansion, 
early recession, and late recession. Nominal sector performance coincides only partially 
with popular expectations. Moreover, industry standard deviations and betas indicate 
no. no. Wald
Sectors/Industries firms obs. mean std.dev. beta p-value mean std.dev. beta
Late Expansion - Stage III:
Agriculture 11 213 0.11 0.22 0.81 0.00 0.10 0.21 0.90
Food Products 80 213 0.07 0.15 0.61 0.00 0.13 0.14 0.71
Candy & Soda 12 166 0.05 0.24 0.74 0.01 0.12 0.22 0.84
Beer & Liquor 15 213 0.10 0.19 0.81 0.00 0.13 0.18 0.82
Tobacco Products 9 213 0.15 0.20 0.40 0.04 0.15 0.20 0.64
Healthcare 74 136 0.09 0.33 1.16 0.02 0.09 0.31 1.17
Medical Equipment 87 213 0.12 0.17 0.86 0.01 0.14 0.19 0.93
Pharmaceutical 130 213 0.10 0.16 0.70 0.01 0.13 0.17 0.86
Coal 8 213 0.21 0.33 1.02 0.00 0.14 0.29 1.08
Petroleum & Natural Gas 136 213 0.11 0.18 0.74 0.00 0.14 0.18 0.83
Industry Averages 0.11 0.22 0.79 0.01 0.13 0.21 0.88
Market 213 0.07 0.15 1.00 0.00 0.12 0.14 1.00
Early Recession - Stage IV:
Utilities 137 53 0.00 0.16 0.76 0.05 0.11 0.13 0.55
Communication 43 53 -0.04 0.14 0.63 0.07 0.10 0.14 0.70
Industry Averages -0.02 0.15 0.70 0.06 0.11 0.14 0.62
Market 53 -0.16 0.16 1.00 0.00 0.12 0.14 1.00
Late Recession - Stage V:
Recreation 32 51 0.64 0.31 1.22 0.00 0.09 0.25 1.18
Entertainment 33 51 0.50 0.31 1.28 0.10 0.14 0.24 1.27
Printing & Publishing 32 51 0.62 0.23 1.03 0.00 0.12 0.20 1.05
Consumer Goods 82 51 0.49 0.21 1.01 0.00 0.12 0.16 0.87
Apparel 57 51 0.63 0.27 1.09 0.00 0.10 0.20 1.05
Rubber & Plastic 29 51 0.42 0.22 0.90 0.00 0.12 0.20 1.05
Textiles 46 51 0.47 0.25 1.09 0.00 0.10 0.20 1.00
Construction Materials 125 51 0.51 0.23 1.17 0.00 0.11 0.18 1.10
Construction 32 51 0.63 0.33 1.51 0.00 0.12 0.24 1.29
Automobiles & Trucks 65 51 0.38 0.25 1.06 0.00 0.11 0.20 1.05
Business Supplies 32 51 0.44 0.24 1.19 0.00 0.11 0.20 1.03
Wholesale 96 51 0.43 0.23 1.06 0.00 0.11 0.19 1.07
Retail 172 51 0.55 0.24 1.11 0.00 0.12 0.18 0.98
Restaurants & Hotels 48 51 0.52 0.28 1.27 0.00 0.12 0.21 1.08
Banking 151 51 0.48 0.23 1.14 0.02 0.12 0.18 0.96
Insurance 77 51 0.44 0.20 0.86 0.01 0.12 0.19 0.89
Real Estate 32 51 0.56 0.31 1.21 0.00 0.07 0.24 1.10
Trading 186 51 0.53 0.23 1.16 0.00 0.14 0.20 1.21
Industry Averages 0.51 0.25 1.13 0.01 0.11 0.20 1.07
Market 51 0.40 0.18 1.00 0.00 0.12 0.14 1.00
Business Cycle Stage Full Sample 1948:01-2007:12 
 145 
that risk-adjusted performance will coincide even less with popular expectations. For 
instance, in early and middle expansion, average industry underperformance coincides 
with average standard deviations higher than the market and above average industry 
betas. For the remaining three stages, the results are inconsistent, with mostly lower 
industry betas but higher industry standard deviations. 
The nominal industry performance results are not encouraging for sector rotation 
investors. Historically, alternative industries do provide performance during early and 
middle expansion, but popular sector rotation strategy does not include those 
industries.88 The next section investigates whether industries provide systematic risk-
adjusted business-cycle performance. 
4.4.4 Risk-adjusted industry performance measures 
Table 4.5 reports industry excess market returns, Jensen’s alphas, Fama and French 
(1992) three-factor alphas, and Carhart (1997) four-factor alphas by business-cycle 
stage.89 The table reports performance alphas estimated with Equations 4.3 to 4.6 
reported as annualized rates. Emboldened alpha estimations indicate statistical 
significance at 10 percent calculated with White (1980) heteroskedasticity consistent t-
statistics. Columns 3, 5, 7, and 9 of Table 4.5 report results from a Wald test of equal 
industry performance across business-cycle stages. Formally stated, Wald p-values 
estimates are under the null hypothesis H0: 1=2=3=4=5, which Table 4.5 reports 
separately for each industry and performance measure. 
Equation 4.3 estimates excess market industry performance (m), with a regression of 
excess market industry returns (ri-rm) on the five business-cycle dummy variables (Ds). 
The regression coefficient mis  measures market outperformance for industry i during 
business cycle stage s. The results show that 10 of 48 industries, approximately one in 
five, generate statistically significant excess market performance when expected. More 
than half of the significant excess market performance occurs in early and late 
                                                 
88 The concluding analysis relaxes the assumption of any sector rotation model, by examining the performance of all 
industries across all stages of the business cycle. 
89 For brevity, the study focuses on the business-cycle stage of expected industry outperformance and provides 
complete results on request. 
 146 
recession. Notably, excess market performance comes before any adjustment for 
systematic exposure to known sources of risk, which the following analysis takes into 
account next. 
5
1
m
it mt is st it
s
r r D 

                 (Eq. 4.3) 
Table 4.5 Industry performance measures by business-cycle stages 
 
Wald Wald Wald Wald
Industries m p-value J p-value F p-value C p-value
Early Expansion - Stage I:
Computers -0.03 0.48 -0.08 0.32 -0.05 0.92 -0.01 0.90
Computer Software -0.12 0.55 -0.17 0.88 -0.18 0.85 -0.19 0.83
Electronic Equip. 0.00 0.29 -0.06 0.94 -0.04 0.94 -0.01 0.90
Measuring & Control -0.06 0.06 -0.10 0.05 -0.07 0.22 -0.06 0.22
Shipping Containers 0.01 0.10 0.01 0.12 -0.01 0.15 -0.01 0.15
Transportation 0.07 0.00 0.07 0.00 0.03 0.08 0.02 0.10
Industry Average: -0.02 -0.05 -0.05 -0.04
Middle Expansion - Stage II:
Chemicals -0.02 0.05 -0.03 0.04 -0.03 0.16 -0.02 0.13
Steel Works -0.01 0.53 -0.03 0.60 -0.06 0.32 -0.05 0.25
Precious Metals -0.05 0.97 -0.03 0.97 -0.07 0.97 -0.07 0.96
Mining -0.01 0.05 -0.03 0.05 -0.06 0.01 -0.06 0.01
Fabricated Products -0.01 0.16 -0.01 0.24 -0.04 0.16 -0.03 0.16
Machinery 0.03 0.02 0.01 0.19 0.00 0.26 0.00 0.22
Electrical Equip. 0.05 0.36 0.02 0.33 0.02 0.39 0.01 0.41
Aircraft 0.05 0.66 0.03 0.84 0.01 0.96 0.00 0.96
Shipbuilding/Railroad -0.05 0.62 -0.07 0.62 -0.08 0.58 -0.08 0.59
Defense 0.01 0.67 0.01 0.48 -0.02 0.52 -0.03 0.48
Personal Services -0.02 0.17 -0.03 0.29 -0.04 0.48 -0.03 0.50
Business Services 0.00 0.09 -0.01 0.13 -0.01 0.16 -0.01 0.17
Industry Average: 0.00 -0.01 -0.03 -0.03
Late Expansion - Stage III:
Agriculture 0.04 0.24 0.04 0.20 0.04 0.15 0.04 0.14
Food Products 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01
Candy & Soda -0.03 0.32 -0.03 0.20 -0.04 0.18 -0.03 0.18
Beer & Liquor 0.03 0.37 0.03 0.65 0.03 0.37 0.04 0.37
Tobacco Products 0.08 0.01 0.09 0.09 0.08 0.05 0.10 0.05
Healthcare 0.01 0.23 0.01 0.30 0.01 0.24 0.02 0.24
Medical Equipment 0.05 0.04 0.05 0.05 0.05 0.10 0.03 0.09
Pharmaceutical 0.03 0.00 0.04 0.01 0.04 0.04 0.01 0.04
Coal 0.14 0.06 0.14 0.09 0.13 0.07 0.06 0.06
Petroleum & Natural Gas 0.05 0.50 0.05 0.42 0.04 0.36 0.03 0.35
Industry Average: 0.04 0.04 0.04 0.03
Early Recession - Stage IV:
Gas & Electric 0.20 0.01 0.13 0.74 0.12 0.49 0.12 0.50
Communication 0.15 0.02 0.05 0.65 0.04 0.74 0.04 0.64
Industry Average: 0.17 0.09 0.08 0.08
Excess Market Jensen's alpha Fama-French alpha Carhart alpha
 147 
Continued: 
 
Notes: Table 4.5 reports industry excess market returns (m), Jensen’s alphas (J), Fama and French (1992) three-
factor alphas (F), and Carhart (1997) four-factor alphas (C) for the business-cycle stages of expected 
outperformance listed in Table 4.1. Equations 4.3–4.6 estimate excess market returns, Jensen’s alphas, Fama and 
French alphas, and Carhart alphas by business-cycle stage. The table reports annualized alpha returns. Emboldened 
alpha performance indicates 10 percent statistical significance estimated with White (1980) heteroskedasticity 
consistent t-statistics. Columns 3, 5, 7, & 9 of Table 4.5 report Wald test p-values under a null hypothesis of constant 
industry performance across all five business-cycle stages. Wald p-values are under the null hypothesis Ho: 
1=2=3=4=5. Table 4.5 also reports equally weighted industry averages beneath each business-cycle stage. 
Equation 4.4 estimates a Jensen’s alphas (J) attributable to each business-cycle 
stage with a modified single-index model.  
5 5
,
1 1
( )Jit t is st m is mt t st it
s s
r rf D r rf D  
 
                 (Eq. 4.4) 
Equation 4.4 runs a regression of industry returns in excess of the one-month 
Treasury bill (ri-rf) on one of five business-cycle timing variables (Ds) and the 
conditional market risk premium (rm-rf). The Fama and French market index represents 
the market proxy. 
To ensure the results do not depend on exposure to other well-known risk factors, the 
analysis also estimates Fama and French three-factor alphas and Carhart four-factor 
alphas. The Fama and French alphas (F), estimated with Equation 4.5, control for size 
and value risk factors in addition to market risk. Lastly, the Carhart four-factor alphas 
Wald Wald Wald Wald
Industries m p-value J p-value F p-value C p-value
Late Recession - Stage V:
Recreation 0.18 0.47 0.10 0.66 0.06 0.56 0.00 0.59
Entertainment 0.07 0.76 -0.01 0.18 -0.05 0.06 -0.04 0.05
Printing & Publishing 0.16 0.06 0.15 0.07 0.12 0.07 0.08 0.07
Consumer Goods 0.07 0.37 0.06 0.28 0.07 0.24 0.00 0.22
Apparel 0.17 0.27 0.14 0.34 0.07 0.27 0.08 0.39
Rubber & Plastic 0.01 0.45 0.04 0.56 0.01 0.47 -0.03 0.47
Textiles 0.05 0.34 0.03 0.34 -0.02 0.81 0.01 0.89
Construction Materials 0.08 0.09 0.03 0.33 0.00 0.29 -0.03 0.31
Construction 0.17 0.00 0.01 0.03 -0.01 0.01 0.03 0.01
Automobiles & Trucks -0.01 0.07 -0.03 0.08 -0.07 0.39 -0.05 0.34
Business Supplies 0.03 0.22 -0.03 0.26 -0.04 0.65 -0.04 0.68
Wholesale 0.02 0.56 0.00 0.75 -0.01 0.72 -0.07 0.72
Retail 0.11 0.10 0.08 0.10 0.04 0.09 0.01 0.10
Restaurants & Hotels 0.09 0.37 0.01 0.57 -0.02 0.60 -0.04 0.66
Banking 0.06 0.43 0.01 0.48 0.00 0.34 -0.05 0.38
Insurance 0.03 0.92 0.07 0.92 0.09 0.82 0.04 0.84
Real Estate 0.11 0.08 0.05 0.14 -0.03 0.10 -0.12 0.11
Trading 0.10 0.02 0.05 0.60 0.03 0.77 0.05 0.82
Industry Average: 0.08 0.04 0.01 -0.01
Excess Market Jensen's alpha Fama-French alpha Carhart alpha
 148 
(C), estimated with Equation 4.6, add a momentum factor to the Fama and French 
three-factor model. 
5 5
1 1
( )F m s vit t is st is mt t is t is t st it
s s
r rf D r rf SMB HML D    
 
                   (Eq. 4.5) 
5 5
1 1
( )C m s v cit t is st is mt t is t is t is t st it
s s
r rf D r rf SMB HML MOM D     
 
                (Eq. 4.6) 
Regardless of the risk-adjusted alpha performance measure, there is scant evidence 
of statistically significant industry outperformance where popular belief would suggest. 
The performance results strengthen the earlier findings reported for nominal returns. 
Based on Jensen’s alphas, there are six industries with significant outperformance. 
Based on the Fama and French three-factor model, there are only five industries with 
significant outperformance, and using the Carhart four-factor model only two. At a 10 
percent significance level, that is what one would roughly expect to observe randomly, 
in the absence of true systematic industry outperformance.90  
An additional step tests for systematic differences in industry performance across all 
five business-cycle stages with Wald tests. Table 4.5 reports Wald test p-values under a 
null hypothesis of constant measures of industry performance across the business cycle. 
If industry outperformance varies across business-cycle stages, one should reject the 
null hypothesis of constant performance. However, the results fail to reject the null 
hypothesis of constant excess market returns for 29 of the 48 industries at a 10 percent 
significance level. Similarly, Wald tests of Jensen’s, Fama-French, and Carhart 
performance alphas indicate acceptance of the null hypothesis of constant industry 
performance roughly 75 percent of the time at 10 percent significance. The Wald tests 
results of constant industry performance differ substantially from Wald tests of constant 
nominal returns. The results show that general market movement and exposure to 
systematic risk factors largely explain differences in industry return performance across 
business cycles. 
                                                 
90 If anything, it seems that the utilities and communications industries perform as popular belief anticipates, showing 
early recession performance. 
 149 
4.5 Sector Rotation Performance 
Can conventional sector rotation still be profitable, despite limited evidence of 
systematic industry performance? This section focuses on strategy implementation, 
observing the performance of sector rotation across the last 10 business cycles. The 
base-case sector rotation strategy assumes investors perfectly time NBER business-
cycle stages and rotate the Fama and French 49 industries following the conventional 
sector rotation depicted in Table 4.1. The strategy invests equally in all industries held 
at each stage. Table 4.6 reports the performance of a conventional sector rotation 
strategy and compares the results with a simple market investment. The table reports 
annualized strategy returns and Jensen’s alphas, with corresponding White (1980) 
heteroskedasticity consistent t-statistics. Table 4.6 also provides strategy Sharpe ratios, 
along with single-index betas and standard deviations. 
Annualized sector rotation outperformance, based on perfectly timing business 
cycles and a conventional sector rotation model, amounts to 2.3 percent.91 At first 
glance, the outperformance appears economically large. However, in perspective, the 
2.3 percent represents the maximum outperformance. Only investors who followed 
popular market wisdom over the past 60 years, ignored transactions costs, and perfectly 
timed the last 10 NBER business cycles, would have realized 2.3 percent 
outperformance. Siegel (1991) suggests a simpler market-timing strategy, showing that 
shifting between equities and cash at business-cycle turning points generates significant 
outperformance. However, Siegel (1991) also recognizes the difficulty in correctly 
timing business cycles. To provide perspective on sector rotation outperformance, the 
results also report the performance of the simpler market-timing strategy suggested by 
Siegel (1991). Here, the analysis assumes a theoretical investor who correctly times 
NBER recessions and expansions. Such an investor, shifting from equities to cash early 
recession and back to equities late recession, would have realized 2.5 percent 
outperformance. That same investor would have also held a more diversified market 
portfolio, subject to less industry-specific risk. The next section considers what happens 
under more realistic assumptions, with the introduction of transaction costs. 
                                                 
91 Since the analysis estimates Jensen’s alphas with a constant beta over the full sample period, outperformance does 
not equal the weighted average of industry outperformance by business-cycle stage reported in Table 4.4. 
 150 
Table 4.6 Performance comparison of alternative investment strategies 
 
Notes: Table 4.6 reports mean returns, standard deviations, single-index betas, Sharpe ratios, and Jensen’s alphas for 
the base-case sector rotation and market-timing strategies with different transaction costs. The table reports mean 
returns, standard deviations, and Jensen’s alphas as annualized rates and White (1980) heteroskedasticity consistent 
Jensen’s alpha t-statistics. The shaded strategy performance results reported in Table 4.6 are for alternative 
sector/industry groupings and business cycle stages that sections 4.6.1 and 4.6.2 discuss later as robustness tests. 
Sharpe  Jensen's  
Strategy mean std.dev. beta ratio alpha t-statistic
Market 11.6% 14.5% 1.00 0.13
0% round-trip transaction costs:
Base case sector rotation 14.2% 16.8% 1.00 0.15 2.3% 1.94
Market-timing 13.6% 13.6% 0.89 0.17 2.5% 3.57
Alternative sector/industry groups
10  Sectors 13.2% 17.3% 1.04 0.13 1.1% 0.94
13 Standard & Poor's sector indices 12.1% 17.8% 0.86 0.11 1.4% 0.77
23 Industry groups 13.9% 16.9% 1.00 0.15 2.1% 1.76
Alternative business cycle stages
2 NBER stages 11.9% 15.9% 1.04 0.12 0.0% -0.02
4 NBER stages 13.5% 16.6% 1.02 0.14 1.6% 1.55
5 CFNAI stages 6.2% 15.4% 0.82 0.03 -3.8% -2.99
0.5% round-trip transaction costs:
Base case sector rotation 13.7% 16.8% 1.00 0.14 1.9% 1.59
Market-timing 13.4% 13.7% 0.89 0.17 2.3% 3.32
Alternative sector/industry groups
10  Sectors 12.7% 17.3% 1.04 0.12 0.7% 0.59
13 Standard & Poor's sector indices 11.7% 17.8% 0.86 0.10 0.9% 0.53
23 Industry groups 13.5% 16.9% 1.00 0.14 1.7% 1.42
Alternative business cycle stages
2 NBER stages 11.7% 15.9% 1.04 0.12 -0.2% -0.27
4 NBER stages 13.2% 16.7% 1.02 0.14 1.2% 1.22
5 CFNAI stages 3.7% 15.3% 0.83 -0.02 -6.1% -4.84
1.0% round-trip transaction costs:
Base case sector rotation 13.2% 16.8% 1.00 0.14 1.5% 1.24
Market-timing 13.2% 13.7% 0.89 0.16 2.1% 3.06
Alternative sector/industry groups
10  Sectors 12.2% 17.3% 1.04 0.12 0.3% 0.24
13 Standard & Poor's sector indices 11.2% 17.8% 0.86 0.10 0.5% 0.30
23 Industry groups 13.0% 16.9% 1.00 0.13 1.3% 1.06
Alternative business cycle stages
2 NBER stages 11.5% 15.9% 1.04 0.11 -0.3% -0.52
4 NBER stages 12.8% 16.7% 1.03 0.13 0.9% 0.88
5 CFNAI stages 1.2% 15.4% 0.83 -0.06 -8.4% -6.63
1.5% round-trip transaction costs:
Base case sector rotation 12.8% 16.8% 1.00 0.13 1.1% 0.89
Market-timing 13.0% 13.7% 0.90 0.16 1.9% 2.78
Alternative sector/industry groups
10  Sectors 11.8% 17.3% 1.04 0.11 -0.1% -0.12
13 Standard & Poor's sector indices 10.8% 17.8% 0.86 0.09 0.1% 0.06
23 Industry groups 12.5% 16.9% 1.00 0.12 0.8% 0.71
Alternative business cycle stages
2 NBER stages 11.3% 15.9% 1.04 0.11 -0.5% -0.77
4 NBER stages 12.4% 16.7% 1.03 0.12 0.5% 0.54
5 CFNAI stages -1.2% 15.5% 0.83 -0.11 -10.6% -8.32
 151 
Transaction costs, both explicit and implicit, are difficult to estimate precisely. 
Estimated transaction costs include commissions, bid-ask spread, and market impact. 
Actual costs depend on the stock, where it trades, and when it trades.92 Estimates vary 
considerably and change over the sample.93 As an allowance, Table 4.7 reports 
performance with round-trip transaction costs that range between 0.5 and 1.5 percent. 
Sector rotation has 50 round-trip transactions and the market-timing strategy has 20 
round-trip transactions from 1948 to 2007. With the inclusion of transaction costs, the 
base-case sector rotation outperformance decreases to between 1.1 to 1.9 percent and 
becomes statistically indistinguishable from zero. The alternative market-timing 
strategy increases in relative outperformance, owing to fewer transactions. 
Thus far, the results indicate only marginal sector rotation outperformance for sector 
rotation implemented in accordance with popular wisdom, even if one assumes 
investors can correctly time business cycles. Results for industries expected to perform 
well in early expansion and middle expansion are particularly disappointing. Still, it 
would be premature to conclude that sector rotation does not work. Investors may use 
different industry or sector classifications, different business-cycle indicators, or 
different business-cycle stages. Alternatively, investors may time business cycles in 
advance or with a delay, which could generate outperformance. 
Robustness tests of the base-case scenario consider alternative data sets, performance 
measures, and sample periods. The robustness tests also investigate whether the results 
improve if investors anticipate changes in business-cycle turning points earlier or later. 
In addition to NBER business cycles, the analysis tests business-cycle stages 
constructed from the CFNAI and a forecast model based on well-known business-cycle 
proxies. Another test separates the sample into two sub-periods and business-cycle 
stages in halves. The analysis concludes with the total relaxation of any specific sector 
rotation model, testing for the systematic performance of any sector across any 
business-cycle stage. 
                                                 
92 See for example Goyenko, Holden, and Trzcinka (2009) and Hasbrouck (2009). 
93 Estimates of total trading costs vary greatly depending on the study. For instance, Lesmond, Schill, and Zhou 
(2004) estimate round-trip transaction costs of 1 to 2 percent for most large-cap trades while Keim and Madhavan 
(1998) estimate total round-trip transaction costs as low as 0.2 percent. 
 152 
4.6 Robustness Checks 
4.6.1 Alternative sector/industry groups and data sets 
There are alternative sector and industry classifications available to sector rotation 
investors. As such, the base-case analysis might merely reflect a particular industry 
grouping. Additionally, the performance of the narrowly defined Fama and French 49 
industry portfolios might unduly reflect idiosyncratic performance. Consequently, one 
can argue that the results merely measure firm specific developments unrelated to the 
business cycle. To accommodate both possibilities, the following analysis investigates 
the performance of three alternative sector and industry groups. 
The analysis first maps the Fama and French 49 industry portfolios to two alternative 
portfolio groupings. There are alternative sector and industry groupings readily 
available on the Kenneth French website. However, the Fama and French classifications 
vary considerably in how they map firms to the “other” industry group. For instance, 
“other” represents an average 27 percent of all NYSE, AMEX, and NASDAQ listed 
firms in the Fama and French 17 industry portfolios from 1948 to 2007. Moreover, 
those firms come from a variety of sectors. In contrast, “other” in the Fama and French 
49 industry group represents 3.5 percent of the total. As such, the omission of such a 
large percent of the market, and important sectors, renders the performance of 
alternative Fama and French portfolios incomparable. 
As an alternative, the analysis maps the original Fama and French 49 industries to 10 
sector portfolios and 23 major industry portfolios, as listed in Table 4.7. The 10 sector 
portfolios are constructed following the Kacperczyk, Sialm, and Zheng (2005) mapping 
of the Fama and French 48 portfolios. The additional computer software industry 
included in the Fama and French 49 industry portfolios goes into the business 
equipment and services sector. Additionally, the analysis maps the Fama and French 49 
industries to one of 23 GICS major industry groups. The Global Industry Classification 
Standard (GICS), first introduced in 1999, provides a widely accepted alternative to SIC 
classifications.94 Bhojraj, Lee, and Oler (2003) report GICS classifications are superior 
                                                 
94 For details, see http://www2.standardandpoors.com/spf/pdf/index/GICS_methodology.pdf 
 153 
to alternative classification schemes. Results obtained from the alternative 10 sector and 
23 major industry portfolios compares directly with the base-case analysis using the 
Fama and French 49 industries. 
Table 4.7 Alternative sector and industry mappings 
 
Notes: Table 4.7 provides a mapping of the Fama and French 49 industry portfolios to 23 Global industrial 
Classification Standard (GICS) industry groups and 10 sector classifications. 
Code Industry Description Code Industry Group Description Code Sector Description
01 Agriculture 3020 Consumer Staples 01 Consumer Non-Durable
24 Aircraft 2010 Industrials 04 Manufacturing
10 Apparel 2520 Consumer Discretionary 01 Consumer Non-Durable
23 Automobiles & Trucks 2510 Consumer Discretionary 02 Consumer Durable
45 Banking 4010 Financials 10 Finance
04 Beer & Liquor 3020 Consumer Staples 01 Consumer Non-Durable
34 Business Services 2020 Industrials 08 Business Equipment & Services
39 Business Supplies 2020 Industrials 04 Manufacturing
03 Candy & Soda 3020 Consumer Staples 01 Consumer Non-Durable
14 Chemicals 1510 Materials 04 Manufacturing
29 Coal 1010 Energy 05 Energy
32 Communication 5010 Telecommunication Services 07 Telecom
36 Computer Software 4510 Information Technology 08 Business Equipment & Services
35 Computers 4520 Information Technology 08 Business Equipment & Services
18 Construction 2550 Consumer Discretionary 04 Manufacturing
17 Construction Materials 1510 Materials 04 Manufacturing
09 Consumer Goods 2530 Consumer Discretionary 02 Consumer Durable
26 Defense 2010 Industrials 04 Manufacturing
22 Electrical Equipment 2010 Industrials 04 Manufacturing
37 Electronic Equipment 4530 Information Technology 08 Business Equipment & Services
07 Entertainment 2540 Consumer Discretionary 01 Consumer Non-Durable
20 Fabricated Products 2010 Industrials 04 Manufacturing
02 Food Products 3010 Consumer Staples 01 Consumer Non-Durable
11 Healthcare 3510 Healthcare 03 Healthcare
46 Insurance 4030 Financials 10 Finance
21 Machinery 2010 Industrials 04 Manufacturing
38 Measuring & Control 4520 Information Technology 08 Business Equipment & Services
12 Medical Equipment 3510 Healthcare 03 Healthcare
28 Mining 1510 Materials 05 Energy
33 Personal Services 2530 Consumer Discretionary 01 Consumer Non-Durable
30 Petroleum & Natural Gas 1010 Energy 05 Energy
13 Pharmaceutical 3520 Healthcare 03 Healthcare
27 Precious Metals 1510 Materials 05 Energy
08 Printing & Publishing 2540 Consumer Discretionary 01 Consumer Non-Durable
47 Real Estate 4040 Financials 10 Finance
06 Recreation 2520 Consumer Discretionary 02 Consumer Durable
44 Restaurants & Hotels 2530 Consumer Discretionary 09 Wholesale & Retail
43 Retail 2550 Consumer Discretionary 09 Wholesale & Retail
15 Rubber & Plastic 2550 Consumer Discretionary 04 Manufacturing
25 Shipbuilding &  Railroad 2010 Industrials 04 Manufacturing
40 Shipping Containers 2030 Industrials 04 Manufacturing
19 Steel Works 1510 Materials 04 Manufacturing
16 Textiles 2520 Consumer Discretionary 01 Consumer Non-Durable
05 Tobacco Products 3020 Consumer Staples 01 Consumer Non-Durable
48 Trading 4020 Financials 10 Finance
41 Transportation 2030 Industrials 04 Manufacturing
31 Utilities 5510 Utilities 06 Utilites
42 Wholesale 2550 Consumer Discretionary 09 Wholesale & Retail
49 Fama-French Industries 23 GICS Major Industry Groups 10 Sectors
 154 
Lastly, the analysis evaluates 13 Standard & Poor’s sector indices. The Standard & 
Poor’s indices provide sector benchmarks, well known by financial practitioners. Sector 
indices are constructed from Standard & Poor’s 500 constituent stocks based on their 
SIC or, if available, GICS sector classification.95 As such, the Standard & Poor’s indices 
comprise large capitalized firms, while the Fama and French portfolios comprise the 
entire market. For comparison with the base case analysis, the analysis only includes the 
13 Standard & Poor’s sector indices with data available from 1948 to 2007. The 
Standard & Poor’s index data comes from Global Financial Data.96 
Table 4.8 compares the summary statistics and performance measures for the 10 
sector portfolios (10FF), 13 Standard & Poor’s sector indices (13SP), and 23 GICS 
major industry groups (23FF). The analysis evaluates the alternative sector and industry 
portfolios by the business-cycle stage popular belief anticipates outperformance will 
occur. Table 4.8 reports mean returns, standard deviations, and single-index betas by 
business-cycle stage and for the full sample. Additionally, Table 4.8 also reports excess 
market performance, Jensen’s alphas, Fama and French alphas, and Carhart alphas. 
Bold indicates statistically significant performance at 10 percent using White (1980) 
heteroskedasticity consistent t-statistics. The table reports returns and performance 
measures as annual rates. The results reported in Table 4.8 directly compare with the 
base-case results reported in Table 4.4 and Table 4.5. 
Nominal returns and alpha performance for the alternative portfolio largely compare 
with the earlier analysis. Generally, nominal returns are slightly lower, compared with 
the Fama and French 49 industries. At the same time, less return volatility and market 
risk indicate the more aggregated sector and major industry group portfolios provide 
diversification benefits. The Standard & Poor’s sector indices, in particular, have the 
least return and beta risk, but also offer the least performance, perhaps reflecting its 
large-cap composition. Similar to the earlier results, the alternative portfolios also 
underperform the market in early and middle expansion. The one exception is the 
average early expansion performance of the Standard & Poor’s indices. Significant 
                                                 
95 Complete details on the construction of Standard & Poor’s sector indices are available at http://www.seemore-
indices.com/assets/files/sectors/pdf/Factsheet_SP_Sector_Indices.pdf  
96 https://www.globalfinancialdata.com 
 155 
excess market performance also largely compares with the Fama and French industries. 
The 23 major industry portfolios provide significant excess market returns about 30 
percent of the time where performance is expected, compared with 20 percent of the 
time for the 10 sector portfolios. The alternative portfolios provide substantially less 
significant Jensen’s, Fama-French, and Carhart alpha outperformance. For instance, 
only one Standard & Poor’s index, railroads in early expansion, generates significant 
Jensen’s alpha outperformance. Table 4.6 provides strategy performance measures for 
sector rotation implemented with the alternative sector and major group industry 
portfolios. The 23 major industry group portfolios provide the highest Jensen’s alpha 
performance, at 2.1 percent, before transaction costs. This performance compares 
unfavorably with the base-case sector rotation performance of 2.3 percent and 2.5 
percent for the market-timing strategy. Strategy performance for the 10 sector portfolios 
and 13 Standard & Poor’s indices is even less, and statistically insignificant. The 
analysis of alternative sector and industry groups shows that the base-case results are 
not specific to a particular industrial classification or data set. 
Table 4.8 Summary statistics for alternative sector and industry groups 
   
Sectors/Industries mean std.dev. beta m J F C mean std.dev. beta
Early Expansion Sectors/Industries- Stage I:
10 FF Sectors Business Equipment & Services 0.11 0.20 1.38 -0.05 -0.09 -0.07 -0.05 0.11 0.22 1.35
13 SP Indices Computer Technology 0.11 0.23 1.21 -0.05 -0.08 -0.05 0.00 0.14 0.23 1.06
Railroads 0.25 0.19 1.05 0.07 0.06 0.03 0.01 0.10 0.20 0.97
Average: 0.18 0.21 1.13 0.01 -0.01 -0.01 0.00 0.12 0.22 1.01
23 FF Groups Transportation 0.21 0.15 0.99 0.04 0.04 0.01 0.00 0.11 0.17 1.04
Software & Services 0.00 0.34 1.71 -0.12 -0.17 -0.18 -0.19 0.02 0.42 1.79
Technology Hardware & Equipment 0.11 0.20 1.37 -0.05 -0.09 -0.06 -0.04 0.13 0.22 1.28
Semiconductors & Equipment 0.17 0.25 1.50 0.00 -0.06 -0.04 -0.01 0.11 0.26 1.44
Average: 0.12 0.24 1.39 -0.03 -0.07 -0.07 -0.06 0.09 0.27 1.39
Middle Expansion Sectors/Industries - Stage II:
10 FF Sectors Manufacturing 0.15 0.16 1.13 0.01 0.00 -0.02 -0.02 0.11 0.17 1.09
13 SP Indices Chemicals 0.11 0.18 1.08 -0.02 -0.03 -0.04 -0.02 0.09 0.18 0.91
Precious Metals 0.10 0.31 0.64 -0.03 0.00 -0.02 -0.02 0.12 0.33 0.50
Metal and Mining 0.14 0.31 1.07 0.00 -0.01 -0.04 -0.03 0.13 0.28 1.05
Capital Goods 0.13 0.16 1.06 -0.01 -0.02 -0.02 -0.01 0.10 0.17 0.97
Average: 0.12 0.24 0.96 -0.02 -0.01 -0.03 -0.02 0.11 0.24 0.86
23 FF Groups Materials 0.12 0.18 1.09 -0.01 -0.02 -0.04 -0.04 0.11 0.18 1.03
Capital Goods 0.15 0.17 1.15 0.01 0.00 -0.02 -0.02 0.11 0.18 1.08
Commercial Services & Supplies 0.12 0.16 1.06 -0.02 -0.02 -0.02 -0.02 0.11 0.17 1.06
Consumer Services 0.15 0.16 1.04 0.01 0.00 0.00 0.01 0.11 0.18 1.01
Average: 0.13 0.17 1.09 0.00 -0.01 -0.02 -0.02 0.11 0.18 1.04
Sample 1948-2007
Performance Measures
Business Cycle Stages 
 156 
Continued: 
 
Notes: Table 4.8 reports mean returns, standard deviations, market betas, excess market alphas, Jensen’s alphas, 
Fama and French alphas, and Carhart alphas for the business-cycle stage popular belief anticipates outperformance 
will occur. Equation 4.2 estimates mean returns and Equations 4.3 to 4.6  estimate the respective performance alphas. 
Table 4.8 also reports mean returns, standard deviations, and market betas for the full 1948–2007 sample period. The 
table reports mean returns, standard deviations, and performance alphas as annual rates and indicate 10 percent 
statistical significance using White (1980) heteroskedasticity consistent t-statistics in bold. 
Sectors/Industries mean std.dev. beta m J F C mean std.dev. beta
Late Expansion Sectors/Industries - Stage III:
10 FF Sectors Consumer Non-Durable 0.06 0.16 0.88 -0.01 0.00 -0.01 0.01 0.12 0.15 0.94
Healthcare 0.10 0.18 0.89 0.03 0.03 0.03 0.02 0.13 0.18 0.98
Energy 0.13 0.19 0.82 0.06 0.06 0.05 0.03 0.12 0.19 0.92
Average: 0.10 0.17 0.86 0.03 0.03 0.03 0.02 0.12 0.18 0.95
13 SP Indices Health 0.12 0.17 0.54 0.05 0.06 0.05 0.04 0.13 0.20 0.71
Oil & Natural Gas 0.08 0.17 0.63 0.01 0.02 0.01 0.00 0.11 0.17 0.74
Consumer Staples 0.05 0.14 0.62 -0.02 -0.01 -0.02 -0.01 0.10 0.15 0.76
Average: 0.08 0.16 0.60 0.02 0.02 0.02 0.01 0.11 0.17 0.74
23 FF Groups Energy 0.16 0.22 0.88 0.09 0.09 0.08 0.04 0.14 0.21 0.96
Food & Staples Retailing 0.07 0.15 0.61 0.00 0.01 0.00 0.01 0.13 0.14 0.71
Food, Beverages, & Tobacco 0.10 0.15 0.68 0.03 0.04 0.03 0.04 0.12 0.15 0.80
Healthcare 0.11 0.21 0.99 0.04 0.04 0.04 0.03 0.13 0.21 1.04
Pharmaceuticals 0.10 0.16 0.70 0.03 0.04 0.04 0.01 0.14 0.17 0.86
Average: 0.11 0.18 0.78 0.04 0.04 0.04 0.03 0.13 0.18 0.87
Early Recession Sectors/Industries - Stage IV:
10 FF Sectors Telecom -0.04 0.14 0.63 0.15 0.05 0.04 0.04 0.10 0.14 0.70
Utilities 0.00 0.16 0.76 0.20 0.13 0.12 0.12 0.11 0.13 0.55
Average: -0.02 0.15 0.70 0.17 0.09 0.08 0.08 0.11 0.14 0.62
13 SP Indices Telecommunications -0.02 0.15 0.49 0.10 0.00 -0.02 -0.02 0.06 0.16 0.60
Gas & Electric Utitlities -0.03 0.17 0.67 0.12 0.07 0.05 0.06 0.06 0.14 0.57
Average: -0.02 0.16 0.58 0.11 0.03 0.01 0.02 0.06 0.15 0.58
23 FF Groups Telecommunication Services -0.04 0.14 0.63 0.15 0.05 0.04 0.04 0.10 0.14 0.70
Utilities 0.00 0.16 0.76 0.20 0.13 0.12 0.12 0.11 0.13 0.55
Average: -0.02 0.15 0.70 0.17 0.09 0.08 0.08 0.11 0.14 0.62
Late Recession Sectors/Industries - Stage V:
10 FF Sectors Consumer Durable 0.50 0.22 1.10 0.07 0.04 0.02 -0.01 0.11 0.18 1.03
Wholesale & Retail 0.50 0.23 1.15 0.07 0.03 0.00 -0.03 0.12 0.17 1.04
Finance 0.50 0.22 1.09 0.07 0.05 0.02 -0.02 0.11 0.17 1.04
Average: 0.50 0.22 1.11 0.07 0.04 0.01 -0.02 0.11 0.17 1.04
13 SP Indices Consumer Durables 0.37 0.19 0.89 -0.02 0.01 -0.01 0.00 0.09 0.16 0.90
Banking 0.29 0.24 0.94 -0.08 -0.06 -0.09 -0.13 0.07 0.19 0.83
Average: 0.33 0.22 0.92 -0.05 -0.03 -0.05 -0.06 0.08 0.17 0.87
23 FF Groups Automobiles & Components 0.38 0.25 1.06 -0.01 -0.03 -0.07 -0.05 0.11 0.20 1.05
Consumer Non-Durables & Apparel 0.58 0.25 1.13 0.13 0.09 0.04 0.03 0.10 0.19 1.08
Media 0.56 0.25 1.15 0.12 0.07 0.03 0.02 0.13 0.20 1.16
Retailing 0.50 0.23 1.15 0.08 0.03 0.01 -0.01 0.12 0.18 1.10
Banks 0.48 0.23 1.14 0.06 0.01 0.00 -0.05 0.12 0.18 0.96
Diversified Financials 0.53 0.23 1.16 0.10 0.05 0.03 0.05 0.14 0.20 1.21
Insurance 0.44 0.20 0.86 0.03 0.07 0.09 0.04 0.12 0.19 0.89
Real Estate 0.56 0.31 1.21 0.11 0.05 -0.03 -0.12 0.07 0.24 1.10
Average: 0.50 0.24 1.11 0.08 0.04 0.01 -0.01 0.11 0.20 1.07
Performance Measures
Sample 1948-2007Business Cycle Stages
 157 
4.6.2 Alternative business-cycle stage delineation 
Arguably, business-cycle stage delineations are arbitrary. Although the five-stage 
analysis follows a common approach, one can potentially construct any number of 
business-cycle partitions. As a result, the base-case results face the criticism that they 
are specific to a particular delineation of business-cycle stages. The NBER officially 
dates U.S. business-cycle peaks and troughs, delineating one stage of expansion and one 
stage of recession. DeStefano (2004) further separates the NBER stages of expansion 
and contraction into two equal halves each, four stages in all. The following analysis 
considers both NBER two-stage and DeStefano (2004) four-stage partitions, to verify 
that the results are robust to alternative business-cycle stage specifications. The two-
stage analysis uses NBER cycle dates to delineate one stage of expansion and one stage 
of recession. Expansions last an average 616 months and recessions last an average 104 
months. The two-stage analysis maps early, middle, and late expansion industries into 
one stage of expansion, and early and late recession industries into one stage of 
recession. The four-stage analysis further divides expansions and recessions in equal 
halves. Early and late expansion stages last on average 302 and 314 months. Early and 
late recession stages last on average 53 and 51 months, identical to the five-stage 
analysis. The four-stage analysis maps middle expansion industries into the late 
expansion stage, with the remaining industries mapped as previous. 
Table 4.9 and Table 4.10 provide summary statistics and performance measures for 
the Fama and French 49 industries across NBER business cycles delineated by two and 
four stages. Similar to the previous analysis, the table reports mean returns, standard 
deviations, and single-index betas by business cycle stage and for the full sample. The 
table also reports Jensen’s, Fama-French, and Carhart alpha performance measures for 
the stage of anticipated outperformance, where bold indicates statistical significance at 
10 percent using White (1980) heteroskedasticity consistent t-statistics. The table 
reports returns and performance measures as annual rates. 
Systematic industry performance does not improve using either the two-stage or the 
four-stage partitions. Nominal industry performance across one stage of expansion now 
equals the market, although with greater return volatility, as Table 4.9 reports. The 
occurrence of systematic excess market returns, and alpha performance, is less with two 
 158 
than five stages. Only two industries in expansions and three in recessions provide 
systematic excess market returns. Moreover, only four industries now provide Jensen’s 
alpha outperformance over both stages of expansion and recession, and even less for 
Fama and French and Carhart alphas. Combining early and late stages of recession 
substantially reduces average excess market performance. Previously, the results 
documented 8 percent average excess market performance in late recession, which now 
drops to an average 2 percent for a single stage of recession. Simply holding utilities 
and telecommunications industries in the early stage of recession proves the superior 
strategy, as popular belief would suggest. Delineating business cycles by four stages, 
reported in Table 4.10, similarly provides no additional insight into systematic industry 
performance. The performance of technology industries improves somewhat with a 
longer stage of early expansion. Nonetheless, nominal industry returns underperform the 
market by 2 percent. Additionally, the four technology industries continue to generate 
significant Jensen’s alpha underperformance. The combined middle and late expansion 
nominal industry returns now outperform the market – but only marginally so. 
Statistically significant excess market and alpha performance measures also show only 
slight improvement over the base-case analysis. Even then, only four of 22 industries 
exhibit systematic excess market performance, compared with three industries over 
middle and late expansion stages previously reported. 
Table 4.6 reports strategy performance measures for sector rotation implementation 
for two-stage and four-stage NBER business cycle delineations. Rotating sectors at 
expansion and recession turning points generate the least performance. And, while the 
DeStefano (2004) four-stage approach realizes a 1.6 percent Jensen’s alpha, 
outperformance is statistically indistinguishable from zero. Both alternative strategies 
thus generate inferior performance, in comparison with the base-case sector rotation. 
Overall, alternative specifications of business-cycle stage partitions provide no 
improvement on the base case and the previous results continue to hold. 
  
 159 
Table 4.9 Two-stage industry summary statistics and performance 
 
Notes: Table 4.9 reports mean returns, standard deviations, single-index betas, excess market returns (m), Jensen’s 
alphas (J), Fama and French alphas (F), and Carhart alphas (c) over NBER delineated stages of expansion and 
recession. The analysis maps previous early, middle, and late expansion industries, listed in Table 4.1, into the stage 
of expansion. Likewise, the analysis maps early and late expansion industries into the stage of recession. Table 4.9 
also reports mean returns, standard deviations, and single-index betas for the full sample 1948–2007. Equation 4.2 
estimates mean returns and Equations 4.3 to 4.6 to estimate performance alphas. The table reports mean returns, 
standard deviations, and performance alphas as annual rates. Bold indicates performance alphas statistically 
significant at 10 percent using White (1980) heteroskedasticity consistent t-statistics. 
Sectors/Industries mean std.dev. beta m J F C mean std.dev. beta
Expansion - Stage I:
Computers 0.13 0.23 1.24 0.01 -0.01 0.03 0.04 0.13 0.24 1.24
Computer Software 0.04 0.38 1.77 -0.07 -0.11 -0.08 -0.08 0.02 0.42 1.79
Electronic Equipment 0.12 0.25 1.46 0.00 -0.03 0.00 0.02 0.11 0.26 1.44
Measuring & Control 0.13 0.22 1.31 0.00 -0.02 0.01 0.01 0.12 0.24 1.32
Shipping Containers 0.12 0.18 0.98 0.00 0.00 -0.01 0.00 0.12 0.18 0.98
Transportation 0.12 0.18 1.05 0.00 -0.01 -0.04 -0.03 0.10 0.19 1.09
Chemicals 0.11 0.17 1.02 -0.01 -0.01 -0.03 -0.02 0.11 0.17 1.01
Steel Works 0.12 0.22 1.29 -0.01 -0.03 -0.05 -0.03 0.10 0.23 1.25
Precious Metals 0.07 0.33 0.59 -0.04 -0.02 -0.04 -0.06 0.08 0.35 0.71
Mining 0.15 0.21 0.99 0.02 0.02 0.00 0.00 0.12 0.21 1.01
Fabricated Products 0.08 0.22 1.04 -0.03 -0.03 -0.06 -0.05 0.05 0.24 1.10
Machinery 0.13 0.18 1.15 0.01 0.00 -0.02 0.00 0.11 0.19 1.17
Electrical Equipment 0.16 0.19 1.20 0.03 0.02 0.02 0.02 0.14 0.20 1.19
Aircraft 0.15 0.22 1.09 0.03 0.02 -0.01 0.00 0.14 0.23 1.12
Shipbuilding &  Railroad 0.11 0.20 0.99 -0.01 -0.01 -0.04 -0.05 0.10 0.21 1.00
Defense 0.12 0.22 0.81 0.00 0.01 -0.03 -0.04 0.12 0.23 0.83
Personal Services 0.09 0.22 1.06 -0.03 -0.03 -0.04 -0.04 0.09 0.23 1.08
Business Services 0.11 0.17 1.07 -0.01 -0.02 -0.02 -0.01 0.10 0.18 1.09
Agriculture 0.12 0.21 0.87 0.00 0.01 0.00 -0.01 0.10 0.21 0.90
Food Products 0.11 0.14 0.70 -0.01 0.01 0.00 0.00 0.13 0.14 0.71
Candy & Soda 0.13 0.21 0.80 0.01 0.02 0.01 0.01 0.12 0.22 0.84
Beer & Liquor 0.12 0.17 0.82 0.00 0.01 0.00 0.00 0.13 0.18 0.82
Tobacco Products 0.13 0.20 0.68 0.01 0.03 0.01 0.03 0.15 0.20 0.64
Healthcare 0.10 0.27 1.11 -0.01 -0.02 -0.04 -0.05 0.09 0.31 1.17
Medical Equipment 0.13 0.18 0.93 0.00 0.01 0.02 0.01 0.14 0.19 0.93
Pharmaceuticals 0.13 0.17 0.88 0.01 0.02 0.03 0.03 0.13 0.17 0.86
Coal 0.16 0.28 1.06 0.04 0.03 0.00 -0.02 0.14 0.29 1.08
Petroleum & Natural Gas 0.16 0.17 0.80 0.03 0.05 0.02 0.01 0.14 0.18 0.83
Industry Average 0.12 0.21 1.03 0.00 0.00 -0.01 -0.01 0.11 0.23 1.04
Market 0.12 0.14 1.00 0.12 0.14 1
Recession - Stage II:
Utilities 0.15 0.16 0.67 0.06 0.07 0.06 0.05 0.11 0.13 0.55
Communication 0.10 0.15 0.60 0.02 0.03 0.02 0.01 0.10 0.14 0.70
Recreation 0.13 0.30 1.21 0.05 0.05 0.06 0.05 0.09 0.25 1.18
Entertainment 0.17 0.30 1.27 0.08 0.08 0.08 0.10 0.14 0.24 1.27
Printing & Publishing 0.14 0.25 1.13 0.05 0.05 0.05 0.04 0.12 0.20 1.05
Consumer Goods 0.16 0.21 0.93 0.07 0.08 0.08 0.07 0.12 0.16 0.87
Apparel 0.13 0.26 1.07 0.05 0.05 0.04 0.07 0.10 0.20 1.05
Rubber & Plastic 0.05 0.23 0.99 -0.03 -0.03 -0.03 -0.03 0.12 0.20 1.05
Textiles 0.08 0.25 1.03 0.00 0.00 -0.01 0.00 0.10 0.20 1.00
Construction Materials 0.09 0.23 1.15 0.01 0.01 0.00 0.00 0.11 0.18 1.10
Construction 0.03 0.31 1.45 -0.04 -0.05 -0.05 -0.03 0.12 0.24 1.29
Automobiles & Trucks 0.04 0.24 0.97 -0.03 -0.03 -0.06 -0.03 0.11 0.20 1.05
Business Supplies 0.07 0.24 1.10 0.00 -0.01 -0.03 -0.03 0.11 0.20 1.03
Wholesale 0.04 0.23 1.05 -0.03 -0.03 -0.03 -0.03 0.11 0.19 1.07
Retail 0.17 0.22 1.03 0.09 0.09 0.08 0.09 0.12 0.18 0.98
Restaurants & Hotels 0.07 0.28 1.25 -0.01 -0.02 -0.01 -0.02 0.12 0.21 1.08
Banking 0.17 0.23 1.06 0.09 0.08 0.08 0.08 0.12 0.18 0.96
Insurance 0.12 0.23 0.96 0.04 0.04 0.05 0.03 0.12 0.19 0.89
Real Estate -0.01 0.32 1.36 -0.08 -0.09 -0.10 -0.12 0.07 0.24 1.10
Trading 0.08 0.24 1.22 0.00 -0.01 -0.01 -0.01 0.14 0.20 1.21
Industry Average 0.10 0.24 1.08 0.02 0.02 0.01 0.01 0.11 0.20 1.02
Market 0.08 0.19 1.00 0.12 0.14 1
Business Cycle Stages Full Sample 1948-2007 
Performance Measures
 160 
Table 4.10 Four-stage industry summary statistics and performance 
   
Sectors/Industries mean std.dev. beta m J F C mean std.dev. beta
Early Expansion - Stage I:
Computers 0.14 0.21 1.29 -0.02 -0.05 -0.03 0.00 0.13 0.24 1.24
Computer Software 0.06 0.31 1.57 -0.07 -0.12 -0.13 -0.13 0.02 0.42 1.79
Electronic Equipment 0.16 0.23 1.41 -0.01 -0.05 -0.03 -0.01 0.11 0.26 1.44
Measuring & Control 0.13 0.21 1.34 -0.03 -0.07 -0.05 -0.04 0.12 0.24 1.32
Shipping Containers 0.17 0.16 0.96 0.01 0.01 0.00 0.00 0.12 0.18 0.98
Transportation 0.22 0.16 1.01 0.04 0.04 0.01 0.00 0.10 0.19 1.09
Industry Average 0.15 0.21 1.26 -0.01 -0.04 -0.04 -0.03 0.10 0.25 1.31
Market 0.17 0.12 1.00 0.12 0.14 1.00
Late Expansion - Stage II:
Chemicals 0.04 0.18 0.98 -0.04 -0.04 -0.05 -0.05 0.11 0.17 1.01
Steel Works 0.09 0.24 1.30 0.01 0.00 0.00 0.00 0.10 0.23 1.25
Precious Metals 0.07 0.35 0.66 -0.03 -0.02 -0.03 -0.06 0.08 0.35 0.71
Mining 0.15 0.22 0.99 0.06 0.06 0.05 0.04 0.12 0.21 1.01
Fabricated Products 0.06 0.23 0.99 -0.03 -0.03 -0.04 -0.02 0.05 0.24 1.10
Machinery 0.09 0.20 1.14 0.01 0.00 0.00 0.02 0.11 0.19 1.17
Electrical Equipment 0.13 0.21 1.23 0.04 0.04 0.04 0.03 0.14 0.20 1.19
Aircraft 0.06 0.24 1.09 -0.02 -0.02 -0.03 -0.03 0.14 0.23 1.12
Shipbuilding &  Railroad 0.07 0.21 0.96 -0.01 -0.01 -0.03 -0.03 0.10 0.21 1.00
Defense 0.06 0.24 0.83 -0.03 -0.03 -0.05 -0.04 0.12 0.23 0.83
Personal Services 0.04 0.25 1.17 -0.04 -0.05 -0.05 -0.03 0.09 0.23 1.08
Business Services 0.07 0.19 1.07 -0.01 -0.01 -0.01 0.00 0.10 0.18 1.09
Agriculture 0.10 0.22 0.86 0.02 0.02 0.01 0.01 0.10 0.21 0.90
Food Products 0.08 0.15 0.68 -0.01 0.00 -0.01 0.00 0.13 0.14 0.71
Candy & Soda 0.11 0.23 0.79 0.01 0.02 0.00 0.02 0.12 0.22 0.84
Beer & Liquor 0.11 0.19 0.84 0.02 0.03 0.02 0.03 0.13 0.18 0.82
Tobacco Products 0.14 0.20 0.55 0.05 0.06 0.05 0.06 0.15 0.20 0.64
Healthcare 0.08 0.31 1.14 -0.02 -0.02 -0.04 -0.03 0.09 0.31 1.17
Medical Equipment 0.11 0.19 0.90 0.03 0.03 0.03 0.01 0.14 0.19 0.93
Pharmaceutical 0.12 0.17 0.79 0.04 0.04 0.04 0.02 0.13 0.17 0.86
Coal 0.20 0.31 1.05 0.11 0.11 0.09 0.03 0.14 0.29 1.08
Petroleum & Natural Gas 0.14 0.18 0.81 0.05 0.06 0.04 0.03 0.14 0.18 0.83
Industry Averages 0.10 0.22 0.95 0.01 0.01 0.00 0.00 0.11 0.22 0.97
Market 0.08 0.15 1.00 0.12 0.14 1.00
Early Recession - Stage IV:
Utilities 0.00 0.16 0.76 0.20 0.13 0.12 0.12 0.11 0.13 0.55
Communication -0.04 0.14 0.63 0.15 0.05 0.04 0.04 0.10 0.14 0.70
Industry Averages -0.02 0.15 0.70 0.17 0.09 0.08 0.08 0.11 0.14 0.62
Market -0.16 0.16 1.00 0.12 0.14 1.00
Business Cycle Stages Full Sample 1948-2007 
Performance Measures
 161 
Continued: 
 
Notes: Table 4.10 reports mean returns, standard deviations, single-index betas, excess market returns (m), Jensen’s 
alphas (J), Fama and French alphas (F), and Carhart alphas (c) over four business-cycle stages. Following 
DeStefano (2004), the analysis divides NBER expansions and recessions into two equal stages each. The analysis 
maps previous middle expansion industries, listed in Table 4.1, into the late expansion stage. Table 4.10 also reports 
mean returns, standard deviations, and single-index betas for the full sample 1948–2007. Equation 4.2 estimates mean 
returns and Equations 4.3 to 4.6 estimate performance alphas. The table reports mean returns, standard deviations, 
and performance alphas as annual rates. Bold indicates performance alphas statistically significant at 10 percent using 
White (1980) heteroskedasticity consistent t-statistics. 
4.6.3 Different ways to measure the business cycle  
This section considers the Chicago Federal Reserve National Activity Index (CFNAI) 
and Conference Board Leading Indicator as alternatives to NBER cycle dates. As results 
for these two indicators are similar, the analysis focuses on the CFNAI.97 In contrast to 
static NBER defined phases of expansion or recession, the CFNAI provides a 
continuous measure of business cycle conditions. The CFNAI incorporates 85 economic 
variables that cover four broad categories: production and income; employment, 
unemployment, and hours; personal consumption and housing; and sales, orders, and 
inventories. CFNAI construction follows the methodology of Stock and Watson (1989), 
who create an index based on the first principal components of a large number of 
                                                 
97 The CFNAI and detrended Conference Board leading indicator have a 78 percent correlation coefficient. Both 
indices thus reveal similar business cycle information,. The study focuses on the CFNAI because it is freely available 
to the public and released monthly by the Chicago Federal Reserve Bank. 
Sectors/Industries mean std.dev. beta m J F C mean std.dev. beta
Late Recession - Stage V:
Recreation 0.64 0.31 1.22 0.18 0.10 0.06 0.00 0.09 0.25 1.18
Entertainment 0.50 0.31 1.28 0.07 -0.01 -0.05 -0.04 0.14 0.24 1.27
Printing & Publishing 0.62 0.23 1.03 0.16 0.15 0.12 0.08 0.12 0.20 1.05
Consumer Goods 0.49 0.21 1.01 0.07 0.06 0.07 0.00 0.12 0.16 0.87
Apparel 0.63 0.27 1.09 0.17 0.14 0.07 0.08 0.10 0.20 1.05
Rubber & Plastic 0.42 0.22 0.90 0.01 0.04 0.01 -0.03 0.12 0.20 1.05
Textiles 0.47 0.25 1.09 0.05 0.03 -0.02 0.01 0.10 0.20 1.00
Construction Materials 0.51 0.23 1.17 0.08 0.03 0.00 -0.03 0.11 0.18 1.10
Construction 0.63 0.33 1.51 0.17 0.01 -0.01 0.03 0.12 0.24 1.29
Automobiles & Trucks 0.38 0.25 1.06 -0.01 -0.03 -0.07 -0.05 0.11 0.20 1.05
Business Supplies 0.44 0.24 1.19 0.03 -0.03 -0.04 -0.04 0.11 0.20 1.03
Wholesale 0.43 0.23 1.06 0.02 0.00 -0.01 -0.07 0.11 0.19 1.07
Retail 0.55 0.24 1.11 0.11 0.08 0.04 0.01 0.12 0.18 0.98
Restaurants & Hotels 0.52 0.28 1.27 0.09 0.01 -0.02 -0.04 0.12 0.21 1.08
Banking 0.48 0.23 1.14 0.06 0.01 0.00 -0.05 0.12 0.18 0.96
Insurance 0.44 0.20 0.86 0.03 0.07 0.09 0.04 0.12 0.19 0.89
Real Estate 0.56 0.31 1.21 0.11 0.05 -0.03 -0.12 0.07 0.24 1.10
Trading 0.53 0.23 1.16 0.10 0.05 0.03 0.05 0.14 0.20 1.21
Industry Averages 0.51 0.25 1.13 0.08 0.04 0.01 -0.01 0.11 0.20 1.07
Market 0.40 0.18 1.00 0.12 0.14 1.00
Performance Measures
Business Cycle Stages Full Sample 1948-2007 
 162 
variables that track economic activity. By construction, the CFNAI has a zero mean and 
unit standard deviation. Positive (negative) CFNAI values indicate above (below) trend 
economic activity. Publication of the CFNAI began in 2001 with data available from 
1967.98 Figure 4.3 overlays the CFNAI on NBER delineated phases of economic 
expansion and contraction (shaded area). The CFNAI closely tracks NBER cycle dates, 
with some variation. The variation may better reflect investor uncertainty when 
attempting to pinpoint real-time changes in business-cycle stages. 
The analysis considers three different approaches to measure industry performance 
related to CFNAI business cycles. Initially, the analysis partitions CFNAI business 
cycles into five equal stages. CFNAI values of 0.702, 0.312, -.0113, and -.637 delineate 
stages of early expansion through late recession. The first approach tests for industry 
outperformance using dummy variables and regressions equivalent to NBER cycles. 
The second approach partitions five CFNAI stages following the Chicago Federal 
Reserve website. The Chicago Federal Reserve defines values above 0.20 as late 
expansion and values below -0.70 as recession. The analysis subdivides Chicago 
Federal Reserve ranges of expansion and recession into additional stages deemed 
representative of business conditions: early and middle expansion split the 0.0 to 0.20 
range and early recession ranges from 0.0 to -0.70. The third approach delineates four 
business-cycle stages based on CFNAI levels and changes. Figure 4.3 indicates that 
positive CFNAI levels and positive changes characterize early expansion; positive 
CFNAI levels and negative changes characterize late expansion; negative CFNAI levels 
and negative changes characterize early expansion; and lastly negative CFNAI levels 
and positive changes characterize late recession. The analysis then runs a regression for 
each industry over the full sample 1968–2007 to test for industry outperformance based 
on CFNAI levels and changes. 
  
                                                 
98 More information is available at http://www.chicagofed.org/economic_research_and_data/cfnai.cfm 
 163 
Figure 4.3 CFNAI delineated business-cycle stages 
 
Notes: Figure 4.3 illustrates the CFNAI economic indicator over the period 1968–2007. Shaded areas indicate NBER 
defined periods of economic contraction. The analysis partitions the range of CFNAI values over 1968–2007 into five 
equal periods of economic activity. The five periods correspond to early expansion (SI), middle expansion (SII), late 
expansion (SIII), early recession (SIV), and late recession (SV). Business-cycle stage delineations are at CFNAI 
values of 0.702, 0.312, -.0113, and -.637 for boundaries SI|SII, SII|SIII, SIII|SIV, and SIV|SV respectively. 
Table 4.11 reports industry performance measures for five CFNAI delineated 
business-cycle stages. The table presents results by the stage of expected performance to 
allow direct comparison with performance results presented for NBER business-cycle 
stages in Table 4.5.99 There is even less systematic industry performance over CFNAI 
stages than NBER stages. No industry provides systematic excess market performance. 
Only two industries (petroleum and retail) provide the expected systematic 
outperformance, based on any of the Jensen’s, Fama-French, and Carhart performance 
measures. Meanwhile, seven industries have systematically negative performance 
alphas. .Columns 3, 5, 7, and 9 of Table 4.11 reports p-values from a Wald test of equal 
industry performance across business-cycle stages. Wald p-values estimates are under 
the null hypothesis H0: 1=2=3=4=5, reported separately for each industry and 
performance measure. For instance, the p-values reject the null of constant market 
                                                 
99 For brevity, the study reports results by the business-cycle stage of expected performance. Complete results are 
available upon request. 
‐4.5
‐3.5
‐2.5
‐1.5
‐0.5
0.5
1.5
2.5
1968 1974 1979 1985 1990 1996 2001 2007
NBER Recessions SI|SII SII|SIII SIII|SIV SIV|SV CFNAI
 164 
outperformance for only seven of the 48 industries at 10 percent statistical significance. 
Table 4.6 compares CFNAI sector rotation performance with alternative sector/industry 
groupings and business-cycle partitions. The CFNAI sector rotation strategy generates a 
statistically significant and negative 3.8 percent Jensen’s alpha – before transaction 
costs. The CFNAI stage results using equal partitions, thus, provide no more insights 
into industry performance or rotation strategies. In unreported results, stage partitions 
based on CFNAI cutoffs also provide no improvement over the base-case analysis. 
Table 4.11 Industry performance over CFNAI business-cycle stages 
   
Wald Wald Wald Wald
Industries m p-value J p-value F p-value C p-value
Early Expansion - Stage I:
Computers 0.04 0.33 0.04 0.24 0.06 0.19 0.02 0.18
Computer Software -0.13 0.56 -0.14 0.24 -0.13 0.30 -0.17 0.46
Electronic Equip. -0.01 0.97 -0.02 0.77 -0.01 0.83 0.01 0.93
Measuring & Control 0.02 0.90 0.01 0.76 0.03 0.55 0.01 0.52
Shipping Containers 0.00 0.88 0.00 0.89 -0.01 0.87 0.02 0.88
Transportation -0.01 0.11 -0.02 0.09 -0.02 0.12 -0.01 0.17
Industry Average: -0.02 -0.02 -0.01 -0.02
Middle Expansion - Stage II:
Chemicals 0.01 0.66 0.01 0.66 0.01 0.61 0.01 0.65
Steel Works 0.02 0.10 0.02 0.09 -0.02 0.18 -0.02 0.18
Precious Metals -0.11 0.12 -0.11 0.12 -0.15 0.17 -0.15 0.16
Mining 0.01 0.28 0.01 0.28 -0.04 0.44 -0.04 0.44
Fabricated Products -0.07 0.41 -0.07 0.41 -0.10 0.58 -0.10 0.55
Machinery 0.02 0.31 0.02 0.25 0.00 0.37 0.00 0.40
Electrical Equip. 0.01 0.94 0.01 0.96 0.00 0.94 0.00 0.94
Aircraft -0.03 0.09 -0.03 0.11 -0.05 0.15 -0.05 0.12
Shipbuilding/Railroad -0.02 0.98 -0.02 0.98 -0.05 0.98 -0.05 0.99
Defense 0.05 0.84 0.05 0.84 0.01 0.81 0.01 0.80
Personal Services -0.05 0.54 -0.05 0.53 -0.06 0.48 -0.06 0.48
Business Services -0.01 0.42 -0.01 0.23 -0.01 0.34 -0.01 0.38
Industry Average: -0.01 -0.01 -0.04 -0.04
Late Expansion - Stage III:
Agriculture -0.04 0.50 -0.02 0.50 -0.03 0.72 0.00 0.63
Food Products -0.02 0.45 0.01 0.43 0.00 0.36 0.00 0.35
Candy & Soda 0.04 0.44 0.05 0.40 0.04 0.47 0.05 0.49
Beer & Liquor 0.02 0.56 0.05 0.47 0.05 0.48 0.06 0.49
Tobacco Products -0.01 0.05 0.00 0.03 -0.04 0.02 0.01 0.02
Healthcare -0.01 0.73 -0.01 0.70 0.01 0.56 0.05 0.52
Medical Equipment -0.03 0.14 -0.02 0.15 0.00 0.12 0.05 0.09
Pharmaceutical 0.02 0.19 0.03 0.22 0.06 0.27 0.05 0.23
Coal 0.06 0.82 0.03 0.84 0.03 0.83 -0.06 0.85
Petroleum & Natural Gas 0.07 0.74 0.11 0.55 0.03 0.62 0.06 0.65
average: 0.01 0.02 0.01 0.03
Early Recession - Stage IV:
Gas & Electric 0.00 0.94 0.03 0.67 -0.03 0.74 -0.02 0.75
Communication -0.07 0.11 -0.08 0.07 -0.08 0.10 -0.06 0.06
average: -0.03 -0.03 -0.05 -0.04
Excess Market Jensen's alpha Fama-French alpha Carhart alpha
 165 
Continued: 
 
Notes: Table 4.11 reports industry excess market returns (m), Jensen’s alphas (J), Fama and French (1992) three-
factor alphas (F), and Carhart (1997) four-factor alphas (C) for the business cycle stages listed in Table 4.1. 
Equations 4.3–4.6 estimate excess market returns, Jensen’s alphas, Fama and French alphas, and Carhart alphas. 
CFNAI data availability limits the sample to the period 1968–2007. The business-cycle dummy variable (Ds) now takes the value of one or zero at time t for one of five CFNAI delineated business-cycle stages. The table reports 
annualized alpha returns. Emboldened alpha performance indicates statistical significance of 10 percent or greater 
estimated with White (1980) heteroskedasticity consistent t-statistics. Columns 3, 5, 7, & 9 of Table 4.11 report Wald 
test p-values under a null hypothesis of constant industry performance across all business-cycle stages, formally 
stated Ho: 1=2=3=4=5. Table 4.11 also reports equally weighted averages beneath each business-cycle stage. 
Table 4.12 provides a measure of industry sensitivity to CFNAI business-cycle 
conditions. Equation 4.7 runs a regression of excess industry returns on a constant, 
CFNAI levels, CFNAI changes, and excess market returns. The regression results cover 
the full 1968–2007 period of CFNAI data availability. For ease of comparison with 
earlier results, the table orders industries by the business-cycle stage popular belief 
anticipates outperformance will occur. Essentially, the CFNAI coefficients measure the 
sensitivity of industry Jensen’s alpha performance attributable to levels and changes in 
business-cycle activity. The analysis tests whether industry sensitivity to CFNAI levels 
and changes occurs jointly, with the correct signs and during the correct business-cycle 
stage. As an example, the computer industry should experience early expansion 
outperformance. Additionally, the CFNAI, by construction, has positive levels and 
positive changes in early expansion. Therefore, the results should indicate significantly 
positive CFNAI level and change coefficients for the computer industry in early 
expansion. Table 4.12 reports block bootstrap p-values from 10,000 replications of 
Wald Wald Wald Wald
Industries m p-value J p-value F p-value C p-value
Late Recession - Stage V:
Recreation 0.07 0.09 0.07 0.05 0.06 0.03 0.06 0.05
Entertainment 0.05 0.00 0.04 0.00 0.05 0.00 0.08 0.00
Printing & Publishing -0.03 0.22 -0.03 0.21 -0.04 0.27 -0.05 0.26
Consumer Goods 0.06 0.55 0.06 0.57 0.05 0.62 0.04 0.61
Apparel 0.07 0.02 0.07 0.02 0.05 0.02 0.07 0.03
Rubber & Plastic -0.01 0.84 -0.01 0.82 -0.01 0.98 -0.03 0.98
Textiles 0.01 0.58 0.01 0.59 0.00 0.63 0.01 0.72
Construction Materials 0.05 0.16 0.05 0.14 0.03 0.06 0.02 0.06
Construction 0.00 0.76 0.00 0.65 -0.01 0.75 -0.01 0.74
Automobiles & Trucks -0.04 0.17 -0.04 0.17 -0.07 0.25 -0.03 0.46
Business Supplies 0.01 0.83 0.01 0.80 -0.02 0.78 -0.02 0.72
Wholesale -0.02 0.15 -0.02 0.13 -0.02 0.30 -0.04 0.22
Retail 0.07 0.02 0.06 0.02 0.06 0.02 0.07 0.04
Restaurants & Hotels 0.05 0.23 0.04 0.17 0.03 0.20 0.02 0.24
Banking 0.02 0.39 0.02 0.39 0.00 0.23 0.01 0.26
Insurance 0.01 0.79 0.02 0.73 0.01 0.86 -0.01 0.87
Real Estate -0.07 0.15 -0.08 0.12 -0.10 0.18 -0.11 0.19
Trading 0.00 0.93 0.00 0.91 0.00 0.90 0.01 0.86
average: 0.02 0.02 0.00 0.00
Excess Market Jensen's alpha Fama-French alpha Carhart alpha
 166 
Equation 4.7 for the joint probability of CFNAI coefficient signs. Shaded areas 
represent the expected sign of CFNAI level and change coefficients. The analysis 
moves middle expansion industries to the late expansion stage, as combinations of 
CFNAI levels and CFNAI changes has only four possible stages. Overall, CFNAI level 
and CFNAI change coefficients are significant at 10 percent about 25 and 33 percent of 
the time. Thus industries, to some degree, do exhibit sensitivity to CFNAI business-
cycle conditions. However, only four industries (recreation, printing & publishing, 
apparel, and textiles), all in late recession, jointly exhibit the expected and statistically 
significant CFNAI coefficient signs. Those same industries have high joint probabilities 
(97 to 100 percent) of having both a negative CFNAI level and CFNAI change 
coefficients, as the last column of Table 4.12 reports. Otherwise, CFNAI business-cycle 
activity does not drive industry outperformance in the way that popular belief suggests. 
Table 4.12 Industry performance sensitivity to CFNAI business cycles 
   
CFNAI ≥ 0 CFNAI ≥ 0 CFNAI < 0 CFNAI < 0 
and and and and
Stage Industry constant CFNAI CFNAI (Mkt-Tbl) CFNAI ≥ 0 CFNAI < 0 CFNAI < 0 CFNAI ≥ 0 
Early Expansion Computers -0.003 0.004 0.003 1.25 0.61 0.32 0.02 0.06
Computer Software -0.009 -0.007 0.035 1.77 0.11 0.00 0.01 0.87
Electronic Equipment -0.003 -0.001 0.012 1.48 0.36 0.01 0.01 0.62
Measuring & Control -0.003 -0.001 0.010 1.39 0.36 0.03 0.02 0.59
Shipping Containers 0.000 -0.001 0.003 0.95 0.21 0.11 0.19 0.49
Transportation -0.001 -0.003 0.012 1.08 0.08 0.00 0.01 0.90
Middle Expansion Chemicals 0.000 -0.002 0.010 0.97 0.14 0.01 0.01 0.85
Steel Works -0.003 0.004 0.005 1.23 0.77 0.19 0.00 0.05
Precious Metals -0.002 -0.004 0.014 0.72 0.19 0.04 0.10 0.66
Mining 0.000 0.001 0.015 1.02 0.64 0.01 0.00 0.35
Fabricated Products -0.006 0.001 0.009 1.09 0.60 0.05 0.02 0.33
Machinery -0.001 0.002 0.008 1.18 0.88 0.01 0.00 0.11
Electrical Equipment 0.002 -0.002 0.010 1.16 0.08 0.00 0.01 0.90
Aircraft 0.000 -0.001 0.018 1.14 0.43 0.01 0.00 0.56
Shipbuilding &  Railroad -0.001 0.002 0.008 1.00 0.62 0.10 0.03 0.24
Defense 0.003 -0.002 0.005 0.83 0.19 0.08 0.19 0.54
Personal Services -0.004 -0.007 0.009 1.16 0.05 0.01 0.09 0.86
Business Services -0.001 -0.003 0.006 1.20 0.04 0.01 0.04 0.91
Late Expansion Agriculture 0.001 0.000 0.002 0.90 0.28 0.20 0.19 0.33
Food Products 0.004 -0.005 -0.008 0.70 0.00 0.00 0.96 0.03
Candy & Soda 0.001 -0.001 -0.015 0.84 0.01 0.41 0.57 0.01
Beer & Liquor 0.003 -0.005 -0.007 0.80 0.00 0.04 0.86 0.10
Tobacco Products 0.006 -0.004 -0.025 0.65 0.00 0.10 0.90 0.00
Healthcare -0.001 -0.010 0.004 1.16 0.01 0.05 0.38 0.57
Medical Equipment 0.002 -0.004 -0.003 0.89 0.01 0.03 0.68 0.28
Pharmaceutical 0.002 0.000 -0.012 0.82 0.00 0.50 0.49 0.00
Coal 0.002 -0.005 -0.006 1.10 0.02 0.16 0.56 0.26
Petroleum & Natural Gas 0.003 0.005 0.000 0.78 0.51 0.46 0.01 0.02
Joint probability of regression coefficient signs 
 167 
Continued: 
 
Notes: Table 4.12 reports regression coefficients estimated with Equation 4.7 over the full sample period 1968-2007. 
CFNAI data availability determines the truncated 1968-2007 sample size. The CFNAI level and change regression 
coefficients effectively measure the sensitivity of industry outperformance attributable to business cycle conditions. 
For ease of comparison with previous tables, the table reports results by the business cycle stage where popular belief 
anticipates industry outperformance will occur. Additionally, Table 4.12 reports block bootstrapped p-values 
estimated with 10,000 replications for the joint probability of CFNAI level and change coefficient signs. Shaded 
areas indicate the anticipated sign of CFNAI level and change coefficients. As an example, the computer industry 
should experience early expansion performance. Furthermore, by construction, the CFNAI has positive levels and 
positive changes in early expansion. Thus, the results should indicate a high probability of positive CFNAI level and 
positive CFNAI change coefficients for the computer industry in early expansion, which Table 4.12 reports at 61 
percent probability. 
0 1 2 ( )mit t i t i t i mt t itr rf CFNAI CFNAI r rf                      (Eq. 4.7) 
4.6.4 Timing the business cycle in advance or with a delay 
Investors might profit from consistently timing the business cycle incorrectly. Suppose 
that investors consistently assume that turning points occur earlier or with a delay from 
actual NBER business cycle dates. If so, the base-case scenario might underestimate 
actual sector rotation outperformance. To explore that possibility, the analysis advances 
the implementation of sector rotation by one month, two months, and three months prior 
to NBER business-cycle turning points. Similarly, the analysis considers delays from 
one to three months. Table 4.13 presents results before transactions costs. 
  
CFNAI ≥ 0 CFNAI ≥ 0 CFNAI < 0 CFNAI < 0 
and and and and
Stage Industry constant CFNAI CFNAI (Mkt-Tbl) CFNAI ≥ 0 CFNAI < 0 CFNAI < 0 CFNAI ≥ 0 
Early Recession Utilities 0.002 -0.002 -0.005 0.51 ` 0.10 0.74 0.14
Communication 0.000 0.002 -0.010 0.75 0.03 0.87 0.10 0.00
Late Recession Recreation -0.003 -0.007 0.017 1.18 0.00 0.00 0.01 0.98
Entertainment 0.002 -0.004 -0.004 1.31 0.03 0.18 0.56 0.23
Printing & Publishing -0.001 -0.005 0.012 1.02 0.02 0.00 0.01 0.97
Consumer Goods 0.000 -0.004 0.003 0.83 0.02 0.01 0.22 0.76
Apparel 0.000 -0.010 0.018 1.11 0.00 0.00 0.00 1.00
Rubber & Plastic 0.000 -0.002 0.010 1.05 0.15 0.01 0.03 0.82
Textiles -0.001 -0.008 0.018 0.98 0.00 0.00 0.00 1.00
Construction Materials 0.000 -0.003 0.003 1.10 0.02 0.01 0.24 0.72
Construction -0.001 -0.004 0.023 1.30 0.12 0.00 0.00 0.88
Automobiles & Trucks -0.002 -0.004 0.007 1.02 0.06 0.01 0.13 0.80
Business Supplies 0.000 -0.001 0.007 0.95 0.34 0.06 0.04 0.56
Wholesale 0.000 -0.002 0.012 1.09 0.12 0.00 0.00 0.88
Retail 0.001 -0.008 0.004 1.03 0.00 0.00 0.20 0.80
Restaurants & Hotels 0.001 -0.005 0.008 1.14 0.06 0.02 0.11 0.81
Banking 0.001 -0.001 -0.010 1.02 0.00 0.25 0.72 0.03
Insurance 0.002 -0.002 0.000 0.90 0.09 0.14 0.37 0.40
Real Estate -0.005 -0.002 0.013 1.09 0.28 0.01 0.02 0.70
Trading 0.002 0.002 -0.002 1.23 0.28 0.61 0.05 0.07
Joint probability of regression coefficient signs 
 168 
Table 4.13 Comparison of strategy performance with different timing 
 
Notes: Table 4.13 reports the performance of sector rotation and market timing with advanced or delayed strategy 
implementation at business cycle stage turning points by the indicated months. The strategy rotates the Fama and 
French 49 industry portfolios according to Table 4.1. Table 4.13 reports mean returns, standard deviations, Sharpe 
ratios, and Jensen’s alphas as annualized rates with White (1980) heteroskedasticity consistent t-statistics. Beta 
estimates come from a single-index model. The reported performance results are before transaction costs. 
Overall, strategy performance declines monotonically and becomes insignificant, 
implementing sector rotation further away from NBER business-cycle stage turning 
points. The 2.3 percent sector rotation Jensen’s alpha decreases to 1.9 and then 1.0 
percent when strategy implementation occurs one, two, and three months earlier. The 
alphas similarly decrease if investors respond with a delay to business-cycles stages. 
The results suggest the importance of precisely timing sector rotation with business-
cycle stages. 
4.6.5 Different business-cycle proxies 
The literature shows certain economic variables track business cycles. The most 
common variables found in the literature are term-spread, default-spread, and dividend 
Sharpe  Jensen's  
Strategy Implementation mean std.dev. beta ratio alpha t-statistic
Market 11.6% 14.5% 1.00 0.13
Sector Rotation:
- 3 months 13.2% 16.5% 0.98 0.14 1.5% 1.27
- 2 months 13.7% 16.6% 0.99 0.14 1.8% 1.53
- 1 month 14.1% 16.8% 1.00 0.15 2.2% 1.81
at turning point 14.2% 16.8% 1.00 0.15 2.3% 1.94
+ 1 month 13.8% 16.8% 1.00 0.14 1.9% 1.63
+ 2 months 13.1% 17.1% 1.03 0.13 1.0% 0.85
+ 3 months 13.0% 17.1% 1.02 0.13 1.0% 0.86
Market Timing:
- 3 months 11.6% 14.1% 0.95 0.13 0.3% 0.63
- 2 months 12.5% 14.2% 0.96 0.15 0.9% 2.25
- 1 month 12.8% 14.2% 0.97 0.15 1.2% 3.10
at turning point 13.6% 13.6% 0.89 0.17 2.5% 3.57
+ 1 month 13.9% 13.5% 0.87 0.18 2.9% 3.75
+ 2 months 13.7% 13.6% 0.88 0.18 2.6% 3.54
+ 3 months 12.8% 13.7% 0.89 0.16 1.7% 2.44
Full Period 1948:01 - 2007:12
 169 
yield.100 If investors use these variables to time sector rotation, it is possible to directly 
test whether a model that predicts relative outperformance, based on these proxies, 
aligns itself with popular belief on sector performance. 
The analysis creates a forecast model with the one-month Treasury bill, term-spread, 
default-spread, and dividend yield as business-cycle variables (BCV).101 Chordia and 
Shivakumar (2002) find that these variables lagged one period predict momentum 
profits related to business cycles. The literature shows that changes in the business-
cycle variables are a proxy for unexpected economic shocks and provide the best asset 
price forecasts.102 As such, the forecast model uses monthly business-cycle variable 
changes. Equation 4.8 first estimates excess industry performance related to changes in 
the business-cycle variables (BCV) lagged one period, after a correction for 
contemporaneous systematic market risk. 
4
0 1
1
( )mit t i jt i mt t it
j
r rf c BCV r rf  

                  (Eq. 4.8) 
The model represents a single-index model modified with the inclusion of lagged 
business-cycle variables. Lagged changes in the business-cycle variables capture the 
relation between business-cycle determinants and future industry Jensen’s alpha 
performance. The forecast model uses the gamma parameter estimates (i) obtained 
from changes in the business-cycle variables to predict a period-ahead Jensen’s alpha. 
Essentially, the gamma estimates represent a decomposed Jensen’s alpha, which allows 
for the contribution of business-cycle determinants to industry outperformance. 
Following the methodology of Chordia and Shivakumar (2002), the forecast model 
estimates the gamma forecast parameters using a 60-month rolling window. The rolling 
window moves forward each month to obtain gamma estimates from the most recent 
60-month window. The model then uses parameter estimates from Equation 4.8 to 
                                                 
100 See for instance, Campbell (1987), Chen (1991), Chen, Roll, and Ross (1986), Fama and French (1989), Jensen, 
Mercer, and Johnson (1996), Keim and Stambaugh (1986), Lewellen (2004), and Petkova (2006). 
101 The analysis calculates the term-spread as the difference between the 10-year Treasury constant maturity yield and 
the three-month Treasury yield. The term-spread is smallest (largest) at NBER defined business cycle peaks 
(troughs). The analysis calculates the default-spread as the difference between low-grade Baa and high-grade Aaa 
corporate bonds. The default-spread increases during periods of recession as investors require greater rates of return. 
102 See, for example, studies by Chen, Roll, and Ross (1986) and Keim and Stambaugh (1986), among others. 
 170 
forecast period-ahead outperformance 1ˆ( )jt  . Equation 4.9 calculates expected industry 
outperformance as the sum of gamma estimates multiplied by current business-cycle 
variable changes. A sector rotation strategy then invests in a portfolio that holds equal 
weights in all industries forecast to have positive outperformance. The following month, 
the process repeats itself once again. The process continues over the entire sample 
period. 
4
1
1
ˆˆ jt i t
i
BCV 

                 (Eq. 4.9) 
An example clarifies the procedure. First, in month 61 the model estimates the i 
parameters from months 1–60. Next, the model multiplies the i parameter estimates by 
BCVi,61, measured as the (BCVi,61-BCVi,60) difference. The i and BCVi,61 product 
forecasts a Jensen’s alpha attributable to business-cycle determinants. Lastly, the model 
includes all industries with positive forecast outperformance (
4
1
ˆ 0i t
i
BCV

 ) in a one-
month sector rotation portfolio. The following month, the model moves the rolling 
window forward one month and then repeats the entire process. 
Panel A and Panel B in Table 4.14 overlay results from the forecast model on 
industry performance with NBER delineated business-cycle stages, for expositional 
purposes. The intention is to observe whether forecast industry performance coincides 
with the period expected by sector rotation investors.103 Panel A reports the average 
number of industries selected for inclusion in the sector rotation portfolio by business-
cycle stage. On average, the forecast model selects approximately half of all industries 
for inclusion in the sector rotation portfolio during any given business-cycle stage, 
suggesting a 50/50 chance of inclusion for each of the 48 industry portfolios. 
Panel B reports the percentage of time the forecast model includes a particular 
industry in the sector rotation portfolio, for the full period and business-cycle stages. If 
                                                 
103 The analysis also overlays the forecast model results on NBER business cycle sub-stages, with each stage divided 
into early and late stages and the sample divided into sub-periods. There is no change in the basic results for both 
sub-stages and sub-periods. 
 171 
business-cycle variables forecast business cycle-related performance and if performance 
aligns with popular belief, the sector rotation portfolio should contain industries during 
the expected stage a high percentage of the time. However, the forecast model selects 
industries for inclusion, more or less, evenly across the business cycle, seemingly 
independent of business-cycle stages. The analysis calculates t-statistics for the 
percentage of time forecast industry performance differs from a 50/50 coin toss. Bold 
indicates percentages significantly different from 50 percent at a 10 percent level of 
statistical significance. Overall, future industry outperformance thus appears unrelated 
to changes in variables known to track the business cycle.104 
Table 4.14 Forecast model sector rotation 
   
                                                 
104 Analogously, in unreported results, additional tests verify whether the CFNAI provides period ahead forecasts of 
industry performance. The results are the same; neither the CFNAI nor the business-cycle variable model provides 
investors with guidance on sector rotation or supports popular belief on business cycle linked performance. 
Panel A:  Average number of  industries selected for inclusion by model 
Full  Early Middle Late Early Late
Period Expansion Expansion Expansion Recession Recession
23 23 23 23 22 23
Panel B: Percentage of time model forecasts period-ahead excess industry returns
Full  Early Middle Late Early Late
Period Industry Period Expansion Expansion Expansion Recession Recession
Early Expansion - Stage I Computers 46 48 48 43 40 48
Computer Software 38 38 37 40 38 33
Electronic Equipment 47 47 47 47 49 50
Measuring & Control 50 50 52 47 43 54
Shipping Containers 48 48 42 54 55 46
Transportation 49 56 46 44 51 57
Middle Expansion - Stage II Chemicals 50 53 49 46 55 46
Steel Works 54 57 56 48 43 67
Precious Metals 40 38 42 40 36 43
Mining 51 53 52 48 51 52
Fabricated Products 40 34 45 44 30 39
Machinery 48 56 45 47 45 39
Electrical Equipment 47 47 47 45 62 43
Aircraft 48 53 48 47 36 50
Shipbuilding &  Railroad 50 48 50 54 36 54
Defense 40 39 41 41 36 39
Personal Services 48 46 52 47 53 43
Business Services 52 54 55 49 36 57
 172 
Continued: 
 
Notes: Table 4.14 reports the composition of sector rotation portfolios constructed with a business-cycle variable 
(BCV) forecast model. The model uses variables found in the literature to forecast stock returns over business cycles. 
The business-cycle variables include lagged changes in the one-month Treasury bill, term-structure, default-spread, 
and dividend yield. Equation 4.8 estimates gamma (i) forecast parameters from a regression of excess industry 
returns (ri-rf) on a constant, lagged changes in the business cycle variables (BCVi), and excess market returns (rm-rf). The model estimates forecast parameters with a 60-month rolling window that moves forward each month. The model then uses parameter estimates to forecast period-ahead industry outperformance, calculated with Equation 
4.9 as the sum of the gamma estimates multiplied by contemporary business-cycle variable changes (BCV). A 
period-ahead sector rotation strategy then includes industries with positive forecast outperformance. Panel A of Table 
4.14 reports the average number of industries included in the portfolio by business-cycle stage. Panel B of Table 4.14 
reports the percentage of time the model selects an industry for inclusion in the portfolio by business-cycle stage. The 
analysis also tests for any difference between the percentage of time the forecast model actually selects an industry 
for inclusion in the portfolio and a random 50/50 probability and indicate 10 percent statistical significance in bold. 
The shaded area in Panel B represents the business-cycle stage where popular belief anticipates outperformance will 
occur listed in Table 4.1. 
  
Panel B: Full  Early Middle Late Early Late
Period Industry Period Expansion Expansion Expansion Recession Recession
Late Expansion - Stage III Agriculture 53 53 57 51 53 52
Food Products 49 48 46 52 49 52
Candy & Soda 39 33 38 43 53 33
Beer & Liquor 49 49 45 50 60 50
Tobacco Products 49 47 47 54 49 52
Healthcare 32 31 37 34 21 22
Medical Equipment 48 40 49 57 53 37
Pharmaceutical 51 45 49 57 57 48
Coal 49 47 49 49 64 41
Petroleum & Natural Gas 52 54 51 51 51 59
Early Recession - Stage IV Utilities 47 39 48 52 55 48
Communication 51 45 57 52 55 52
Late Recession - Stage V Recreation 48 55 43 47 36 52
Entertainment 46 47 48 42 43 57
Printing & Publishing 48 51 48 48 36 43
Consumer Goods 48 48 47 45 51 54
Apparel 52 55 51 52 45 50
Rubber & Plastic 50 60 48 48 40 39
Textiles 49 57 47 46 40 43
Construction Materials 48 49 42 49 47 54
Construction 49 51 49 50 40 48
Automobiles & Trucks 52 56 49 53 43 54
Business Supplies 50 55 51 45 49 46
Wholesale 49 50 52 45 51 48
Retail 48 45 48 48 57 48
Restaurants & Hotels 50 52 53 44 49 57
Banking 51 44 52 57 53 46
Insurance 50 41 55 56 55 37
Real Estate 51 56 52 49 36 54
Trading 53 51 58 51 49 54
 173 
4.6.6 Description of other robustness tests 
4.6.6.1 Sequential industry performance 
Conventional sector rotation presupposes the sequential performance of sectors across 
business-cycle stages. For instance, Standard & Poor’s sequencing in Figure 4.1 shows 
that performance in the technology sector follows performance in the financial sector, 
which in turn follows performance in the utilities sector. Figure 4.1 further illustrates 
other representative sequential patterns of sector performance. While Figure 4.1 depicts 
largely congruent beliefs on sequential sector performance, other variations are possible. 
Moreover, although the analysis considers alternative stages, the actual progression of 
sector performance across business cycles may not fully align with those partitions. To 
overcome such obstacles, the ultimately analysis relaxes any assumed pattern of 
sequential performance and completely ignores business-cycle stages. The analysis tests 
whether the excess market returns of one sector predict future excess market returns of 
other sectors at different lags. The analysis examines lags from one to 24 months, to 
allow for different performance sequencing and business-cycle stage durations. 
Figure 4.4 illustrates the distribution of t-statistics for cross-sector predictability of 
excess market sector performance. First, the analysis maps the Fama and French 49 
industries to 10 equally weighted sector portfolios following Table 4.1. Next, the 
analysis runs individual regressions of excess market sector returns on the excess 
market returns of the remaining sectors at lags from one to 24 months. In total, there are 
2,160 (10 x 9 x 24) t-statistics, covering all possible combinations of sectors and lags. 
Figure 4.4 compares the resultant t-statistic distribution against an expected normal 
distribution. The figure illustrates that the distribution of t-statistics for excess market 
predictability follows a normal distribution. Under a normal distribution and a 10 
percent significance level, the estimations should indicate 5 percent positive 
significance and 5 percent negative significance – even in the absence of actual excess 
market predictability. The bottom of Figure 4.4 reports the percentage of positive and 
negative t-statistics significant at 10 percent for each lag and for collectively for all lags. 
In total, t-statistics are significantly positive 6 percent of the time and significantly 
negative 5 percent of the time. Most significant predictability occurs at a one-month lag, 
indicating some short-term cross-sector momentum. Cross-sector predictability is only 
 174 
marginally higher than a normal distribution. As such, the results suggest that cross-
sector predictability occurs only randomly, without indicating any real evidence of 
statistically significant sequential sector performance. 
Figure 4.4 Predictability of excess market industry performance 
 
Notes: Figure 4.4 illustrates the distribution of t-statistics for cross-sector predictability of excess market 
performance. The analysis constructs sector rotation portfolios from the Fama and French 49 industries mapped to 
one of 10 GICS sectors reported in Table 4.7. The analysis tests lags from one to 24 months to allow for the 
possibility of different performance sequencing and business-cycle stage durations. To illustrate, the Standard & 
Poor’s sequence in Figure 4.1 shows financial sector performance precedes technology sector performance. As such, 
financial sector returns should predict subsequent technology sector returns. There are 2,160 t-statistics, covering all 
possible combinations of cross-sector predictability at up to 24 lags. The bottom of Figure 4.4 reports the total 
percentage of positive and negative t-statistics by lag and in total that are significant at 10 percent. 
4.6.6.2 Sub-stages 
Further analysis also considers different variations of business-cycle stages that might 
improve the base-case scenario. Outperformance potentially occurs at the beginning or 
end of each stage. Considering this, the analysis divides all stages into early and late 
halves then reruns the main tests for sub-stages. There is no significant difference 
between first- and second-half returns across stages. Investors might anticipate different 
stages and react in shorter intervals around business-cycle turning points, rather than 
0%
5%
10%
15%
20%
25%
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Statistical Significance of Excess Market Cross-Sector Predictability
Actual distribution of excess market t-sta tistics Expected distribution of excess market t-statistics assuming a  normal distribution
L(1) L(2) L(3) L(4) L(5) L(6) L(7) L(8) L(9) L(10) L(11) L(12) L(13) L(14) L(15) L(16) L(17) L(18) L(19) L(20) L(21) L(22) L(23) L(24)
Positive (% ) 0.16 0.08 0.10 0.06 0.02 0.12 0.04 0.06 0.03 0.03 0.07 0.02 0.10 0.08 0.09 0.06 0.08 0.04 0.04 0.04 0.07 0.06 0.01 0.07
Negative (% ) 0.09 0.02 0.02 0.06 0.08 0.04 0.03 0.03 0.07 0.03 0.02 0.06 0.10 0.09 0.07 0.04 0.02 0.06 0.03 0.12 0.06 0.02 0.09 0.03
Total Positive (% ) 0.06
Total Negative (% ) 0.05
 175 
over the full length of a stage. The analysis also considers shorter periods, testing for 
significant outperformance two, four and six months around turning points only. Again, 
there is no evidence of significant outperformance. 
4.6.6.3 Sub-samples 
Significant events over the full 60-year sample, like the 1970s bear market and 1990s 
dot.com bubble could potentially drive the results. Unreported analysis also compares 
average industry performance for each business-cycle stage over the 1948–1977 and 
1978–2007 sub-periods. Industry outperformance appears relatively constant across all 
sub-periods and business-cycle stages, regardless of the performance metric. Consistent 
with the previous analysis, early-expansion and middle-expansion industries provide 
inferior outperformance across sub-periods. Overall, the results are not specific to a 
particular sub-period. 
4.6.6.4 Alternative performance measures 
This section evaluates two alternative performance measures to compare base-case 
sector rotation, market-timing, and buy-and-hold strategies. The Goetzmann, Ingersoll, 
Spiegel, and Welch (2007) manipulation-proof performance measure (MPPM) 
eliminates any bias in strategy performance attributable to non-normal iid return 
distributions. Goetzmann, Ingersoll, Spiegel, and Welch (2007) show the MPPM is 
superior to standard performance metrics, such as Sharpe ratios and Jensen’s alphas, 
when return distributions are potentially non-normal. The MPPM estimates portfolio 
performance after adjusting for benchmark risk and is comparable with the proprietary 
Morning Star Risk Adjusted Rating (MRAR).105 Equation 4.10 defines the MPPM 
performance measure ( ) as follows: 
1
1
1 ln( [(1 ) / (1 )] )(1 )
T
t t
t
r rft




                (Eq. 4.10) 
                                                 
105 For an explanation of widely used MRAR see http://www.morningstar.se/aboutus/MRAR_defined.pdf 
 176 
where t is the strategy holding period measured as a percentage of a full year, rt 
strategy returns, rft the risk-free rate, and  benchmark risk. The interpretation of the 
MPPM performance measure is the annualized strategy return premium after adjusting 
for benchmark risk. Goetzmann, Ingersoll, Spiegel, and Welch (2007) estimate that 
benchmark risk ranges from 2 to 4 for the CRSP value-weighted market index. 
Unreported MPPM test results show that the market-timing strategy outperforms both 
the buy-and-hold and sector rotation strategies for a normal range of performance risk 
measures. For instance, when benchmark risk equals 3, market-timing has an annualized 
risk-adjusted performance of 5.2 percent, in comparison to 3.1 percent and 4.1 percent 
for the buy-and-hold and sector rotation strategies. Moreover, MPPM performance 
results show that sector rotation is inferior to market-timing at all levels of benchmark 
risk. 
As another alternative measure, the Barrett and Donald (2003) stochastic dominance 
test evaluates strategy performance independent of benchmarks. Barrett and Donald 
(2003) argue that a stochastic dominance approach is suitable for investors who seek to 
maximize expected utility. Barrett and Donald (2003) show that, due to omitted risk 
factors, a stochastic dominance performance ranking is superior to mean-variance 
measures, such as Jensen’s alphas and Sharpe ratios. Based on unreported test results, 
the market-timing strategy second-order stochastically dominates both base-case sector 
rotation and buy-and-hold strategies. Interestingly, results show, from a stochastic 
dominance perspective, that an investor would be indifferent between sector rotation 
and a buy-and-hold strategy. In conclusion, both the MPPM and second-order stochastic 
dominance performance metrics show that base-case sector rotation is an inferior 
strategy – even for investors who are able to accurately time all business-cycle stages. 
4.7 General Sector Performance across the Business Cycle 
So far, the base-case analysis uncovers no evidence supporting popular belief on sector 
performance or the practicality of conventional sector rotation. There are alternative 
sector rotation variations, as Figure 4.1 illustrates. Thus, the base-case results are 
subject to the criticism of being limited to a specific sector rotation model. What is 
more, the popularity of sector rotation suggests investors can profit from its 
implementation. To account for such possibilities, this section focuses on a general 
 177 
analysis of sector performance. The analysis tests for the systematic performance of any 
sector across any business-cycle stage seeking evidence of an alternative sector rotation 
strategy. 
4.7.1 Relative industry performance rankings 
Initially, the analysis takes a simple, non-parametric view of relative industry 
performance across business cycles. At a basis, sector rotation holds that the relative 
outperformance of sectors/industries shifts across business-cycle stages – for example, 
that early recession performance in transportation precedes middle expansion 
performance in industrials. If so, the excess market performance of certain industries, on 
average, should rank highly during particular stages of the business cycle. 
Table 4.15 reports the average monthly excess market rankings from highest (1) to 
lowest (48) for 48 Fama and French industries, over both the full 1948–2007 sample 
and the original five business-cycle stages. As a guide, the table shades the business-
cycle stage where popular belief anticipates industry performance will occur. The 
results reported in Table 4.15 are striking: The ranking of excess market industry 
performance is, on average, average. There is no evidence of either consistent 
outperformance or underperformance for any industry. Most industry performance ranks 
in the middle (24) for the full period and all stages. An industry that provides systematic 
performance during a particular stage should rank, for example, consistently in the 
upper quartile, or top 12 industries. Rather, the results indicate that relative industry 
performance occurs randomly. 
  
 178 
Table 4.15 Average ranking of excess market industry performance 
 
Notes: Table 4.15 reports realized industry rankings from the highest excess market performance (1) to the lowest 
excess market performance (48). Shading indicates the business-cycle stage where popular belief anticipates excess 
market outperformance will occur. 
Sector/Industry Full Stage I Stage II Stage III Stage IV Stage V
Computers 23 24 23 23 23 22
Computer Software 25 27 24 24 28 23
Electronic Equipment 24 23 23 24 26 21
Measuring & Control 23 25 22 23 25 22
Shipping Containers 23 23 23 23 19 23
Transportation 24 22 24 26 27 25
Chemicals 24 23 25 25 21 27
Steel Works 24 23 24 24 27 26
Precious Metals 25 25 26 25 23 24
Mining 24 23 25 22 25 26
Fabricated Products 26 26 26 25 30 27
Machinery 24 23 22 24 27 25
Electrical Equipment 23 23 22 22 24 25
Aircraft 23 22 22 25 24 23
Shipbuilding &  Railroad 24 23 26 24 25 26
Defense 24 24 24 26 23 23
Personal Services 24 24 24 25 23 21
Business Services 24 24 24 23 24 22
Agriculture 24 25 23 23 26 25
Food Products 23 24 24 24 17 23
Candy & Soda 24 25 21 25 23 24
Beer & Liquor 23 24 24 22 20 23
Tobacco Products 23 25 23 22 17 24
Healthcare 24 25 25 23 27 20
Medical Equipment 23 24 23 23 19 22
Pharmaceutical 23 25 22 23 19 25
Coal 24 23 24 22 27 23
Petroleum & Natural Gas 23 22 24 23 25 25
Utilities 24 24 25 24 17 26
Communication 24 24 25 23 19 28
Recreation 24 24 25 24 24 21
Entertainment 23 24 24 23 20 24
Printing & Publishing 23 24 23 25 24 19
Consumer Goods 24 24 23 25 20 23
Apparel 24 24 24 25 24 20
Rubber & Plastic 23 23 22 24 26 25
Textiles 24 23 25 25 24 22
Construction Materials 24 23 23 25 24 23
Construction 24 25 24 23 29 22
Automobiles & Trucks 24 21 25 25 24 24
Business Supplies 24 22 25 25 24 24
Wholesale 24 24 24 24 25 23
Retail 24 23 25 23 21 22
Restaurants & Hotels 23 24 23 23 26 23
Banking 23 23 24 24 18 23
Insurance 24 23 25 24 22 24
Real Estate 25 24 25 24 28 22
Trading 23 21 23 24 25 21
 179 
Figure 4.5 provides a representative illustration, using the computer hardware 
industry ranking over 201 months of early recession. The figure illustrates, for instance, 
that the performance ranking of computer hardware ranges from the highest rank for 
two months to the lowest rank for 5 months, with an average early expansion rank of 24. 
The performance ranking of computer hardware in early recession repeats a similar 
pattern for the other business-cycle stages. The remaining industries also repeat similar 
patterns of realized performance rankings across all business-cycle stages. Thus, the 
ranking of excess market returns provides no additional insight into where a particular 
industry provides relative business-cycle performance. 
Figure 4.5 Performance ranking for the computer hardware industry 
 
Notes: Figure 4.5 illustrates the excess market performance ranking of the computer hardware industry during 201 
months of early expansion. 
4.7.2 Percentage of months with excess market performance 
This section examines the percentage of months that industries generate excess market 
returns for all business-cycle stages. The intention is to observe whether any industry 
realizes outperformance a high percentage of the time during any business-cycle stage. 
Such evidence would suggest an alternative sector rotation strategy. For instance, an 
industry that outperforms the market 90 percent of early recession months would 
provide a relatively certain sector rotation investment during that stage. 
0
1
2
3
4
5
6
7
8
9
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Months
Excess market performance ranking from highest (1) to lowest (48) 
Excess Market Ranking - Computer Hardware Industry (Stage I)
Rank
 180 
Table 4.16 reports the percentage of months realized industry returns exceed the 
market, for all industries, for all business-cycle stages, and for the full sample. The 
analysis tests the statistical significance of realized excess market performance against a 
50/50 random probability, where bold indicates 10 percent statistical significance. Table 
4.16 also reports annualized excess market industry performance estimated with 
Equation 4.3, where bold indicates 10 percent statistical significance based on White 
(1980) heteroskedasticity consistent t-statistics. 
Some industries do indicate statistically significant market outperformance a high 
percentage of the time during certain stages. Food products, for instance, provide 19 
percent annual excess market performance 77 percent of early recession months. 
Interestingly, popular belief suggests food products provide late expansion rather than 
early recession performance. Similarly, utilities outperform the market 72 percent of 
early recession months, with an average 20 percent annual excess market performance. 
Here the performance occurs precisely where popular belief suggests. Other industries 
also significantly outperform the market a high percentage of the time during different 
business-cycle stages. A majority of significant industry outperformance occurs during 
the stages of early and late recession. Overall, 8 percent of all industries realize market 
outperformance statistically different from random chance at the 10 percent significance 
level for more than 50 percent of the business-cycle stage months. At the same time, 15 
percent of all industries realize systematic excess market performance across full 
business-cycles stages. However, with exceptions, the observed patterns of industry 
performance are largely unsupportive of conventional views on sector rotation. The 
evidence, nonetheless, suggests the possibility of an alternative sector rotation strategy, 
which the next section explores. 
  
 181 
Table 4.16 Percentage of months realized industry returns exceed the market 
 
Notes: Table 4.16 reports the percentage of months realized industry returns exceed the market. The analysis tests the 
statistical significance of realized excess market performance against a 50/50 random probability, where bold 
indicates 10 percent statistical significance. The table also reports annualized excess market performance estimated 
with Equation 4.3. Bold indicates White (1980) heteroskedasticity consistent t-statistics significant at 10 percent. 
  
early recession late recession
% month excess % month excess % month excess % month excess % month excess % month excess
Industries positive market positive market positive market positive market positive market positive market
Computers 0.52 0.014 0.47 -0.034 0.50 0.022 0.55 0.038 0.47 -0.025 0.63 0.118
Computer Software 0.49 -0.073 0.45 -0.120 0.52 -0.020 0.50 -0.069 0.39 -0.340 0.56 0.214
Electronic Equipment 0.51 -0.003 0.49 -0.003 0.53 0.025 0.48 -0.021 0.49 -0.116 0.61 0.092
Measuring & Control 0.49 0.004 0.45 -0.060 0.53 0.046 0.50 0.029 0.45 -0.097 0.49 0.116
Shipping Containers 0.53 0.007 0.51 0.006 0.52 0.019 0.53 -0.029 0.66 0.110 0.49 0.009
Transportation 0.48 -0.011 0.55 0.071 0.50 -0.008 0.44 -0.066 0.28 -0.120 0.47 0.030
Chemicals 0.50 -0.005 0.51 0.030 0.51 -0.018 0.47 -0.030 0.55 0.071 0.47 -0.062
Steel Works 0.46 -0.017 0.51 0.000 0.48 -0.007 0.43 -0.010 0.32 -0.111 0.43 -0.055
Precious Metals 0.48 -0.027 0.47 -0.052 0.44 -0.050 0.50 -0.021 0.48 0.052 0.56 0.094
Mining 0.50 0.001 0.54 -0.005 0.45 -0.012 0.55 0.080 0.42 -0.165 0.45 -0.053
Fabricated Products 0.45 -0.051 0.47 -0.056 0.46 -0.012 0.46 -0.026 0.27 -0.274 0.47 -0.095
Machinery 0.48 -0.005 0.46 0.009 0.55 0.027 0.47 -0.013 0.36 -0.126 0.49 -0.017
Electrical Equipment 0.53 0.024 0.50 0.013 0.58 0.047 0.53 0.042 0.45 -0.071 0.47 -0.001
Aircraft 0.52 0.023 0.55 0.052 0.55 0.049 0.47 -0.013 0.49 -0.044 0.55 0.036
Shipbuilding &  Railroad 0.49 -0.018 0.50 0.013 0.47 -0.053 0.51 0.002 0.45 -0.078 0.51 -0.024
Defense 0.50 0.014 0.52 0.031 0.53 0.014 0.43 -0.037 0.52 0.026 0.63 0.220
Personal Services 0.49 -0.025 0.46 -0.023 0.51 -0.016 0.43 -0.047 0.51 -0.118 0.71 0.138
Business Services 0.49 -0.010 0.46 -0.044 0.53 -0.004 0.51 0.007 0.34 -0.066 0.49 0.090
Agriculture 0.49 -0.013 0.45 -0.061 0.51 0.016 0.51 0.042 0.43 -0.131 0.47 -0.028
Food Products 0.49 0.009 0.45 -0.037 0.45 0.001 0.49 0.003 0.77 0.194 0.53 0.072
Candy & Soda 0.52 0.015 0.46 -0.024 0.63 0.091 0.46 -0.034 0.48 0.011 0.59 0.069
Beer & Liquor 0.52 0.009 0.48 -0.034 0.51 0.009 0.55 0.029 0.58 0.069 0.55 0.037
Tobacco Products 0.53 0.027 0.43 -0.077 0.54 0.020 0.56 0.081 0.70 0.275 0.55 0.040
Healthcare 0.49 -0.017 0.49 -0.015 0.47 -0.043 0.51 0.014 0.39 -0.314 0.66 0.359
Medical Equipment 0.53 0.022 0.47 -0.051 0.53 0.014 0.55 0.050 0.62 0.137 0.59 0.131
Pharmaceutical 0.52 0.017 0.48 -0.067 0.55 0.064 0.51 0.032 0.66 0.136 0.47 -0.001
Coal 0.49 0.022 0.50 0.001 0.49 -0.028 0.53 0.139 0.36 -0.202 0.49 0.111
Petroleum & Natural Gas 0.51 0.020 0.53 0.035 0.49 0.017 0.53 0.046 0.43 -0.037 0.53 -0.067
Utilities 0.49 -0.002 0.46 -0.029 0.48 -0.013 0.50 0.003 0.72 0.199 0.41 -0.062
Communication 0.47 -0.013 0.47 -0.037 0.43 -0.028 0.53 0.007 0.58 0.151 0.31 -0.099
Recreation 0.48 -0.023 0.48 -0.040 0.46 -0.024 0.48 -0.038 0.43 -0.060 0.55 0.178
Entertainment 0.51 0.018 0.46 -0.011 0.51 0.027 0.52 0.006 0.57 0.096 0.53 0.073
Printing & Publishing 0.51 0.001 0.49 -0.017 0.51 0.031 0.49 -0.034 0.47 -0.040 0.71 0.161
Consumer Goods 0.49 0.005 0.45 -0.004 0.49 0.010 0.46 -0.025 0.64 0.081 0.55 0.066
Apparel 0.46 -0.016 0.42 -0.012 0.47 -0.020 0.44 -0.048 0.43 -0.054 0.63 0.169
Rubber & Plastic 0.49 0.003 0.51 -0.002 0.57 0.042 0.46 -0.011 0.36 -0.071 0.41 0.013
Textiles 0.48 -0.018 0.53 0.029 0.50 -0.036 0.41 -0.054 0.47 -0.047 0.55 0.054
Construction Materials 0.48 -0.003 0.48 -0.001 0.51 0.022 0.46 -0.036 0.51 -0.054 0.45 0.083
Construction 0.48 0.001 0.46 -0.036 0.50 0.009 0.53 0.051 0.28 -0.209 0.53 0.166
Automobiles & Trucks 0.50 -0.007 0.57 0.078 0.46 -0.040 0.47 -0.040 0.43 -0.053 0.53 -0.014
Business Supplies 0.48 -0.004 0.52 0.053 0.46 -0.028 0.45 -0.034 0.45 -0.037 0.55 0.029
Wholesale 0.48 -0.005 0.45 -0.011 0.51 0.006 0.45 0.004 0.42 -0.082 0.61 0.020
Retail 0.50 0.000 0.50 -0.016 0.45 -0.038 0.52 0.012 0.57 0.062 0.55 0.111
Restaurants & Hotels 0.51 0.006 0.48 -0.005 0.54 0.025 0.53 0.008 0.42 -0.099 0.51 0.090
Banking 0.52 0.006 0.53 -0.011 0.45 -0.004 0.51 -0.004 0.72 0.114 0.53 0.056
Insurance 0.49 0.001 0.50 -0.015 0.50 -0.011 0.46 0.009 0.55 0.040 0.51 0.031
Real Estate 0.48 -0.038 0.49 -0.030 0.48 -0.025 0.47 -0.038 0.45 -0.237 0.57 0.115
Trading 0.54 0.023 0.56 0.048 0.55 0.034 0.50 0.001 0.45 -0.089 0.61 0.098
Outperform 2.00 2.00 2.00 4.00 3.00 4.00 1.00 3.00 8.00 8.00 5.00 6.00
Underperform 3.00 2.00 2.00 5.00 1.00 1.00 6.00 1.00 8.00 11.00 1.00 1.00
full period middle expansion late expansionearly expansion
 182 
4.7.3 Alternative sector rotation strategy  
As a final step, the analysis investigates whether the opportunity exists for an alternative 
sector rotation strategy. Previously, the results show limited evidence of systematic 
industry performance over 10 business cycles from 1948 to 2007. The true test, of 
course, is whether evidence of in-sample industry performance leads to profitable out-
of-sample sector rotation. To explore that possibility, the analysis first identifies the 
industries that provide systematic performance over the first nine business cycles since 
1948. The analysis then uses the most recent business cycle to test whether any 
identified industries translate into a profitable out-of-sample alternative rotation 
strategy. 
First, the analysis examines the Fama and French 48 industries for evidence of 
statistically significant excess market and Jensen’s alpha performance across all five 
business-cycle stages. Figure 4.6 plots the actual distribution of outperformance 
t-statistics against the expected distribution under a normal distribution. Under the null 
hypothesis of no significant outperformance, the outperformance t-statistics should 
follow a normal distribution centered on zero. Slightly more industries provide 
systematic outperformance than expected under the null hypothesis at a 10 percent 
confidence level. In total, the results show significant excess market and Jensen’s alpha 
outperformance for 29 and 21 industries, rather than 12 each as expected under a normal 
distribution. An alternative sector rotation strategy includes those industries with jointly 
significant excess market and Jensen’s alpha performance. 
  
 183 
Figure 4.6 Distribution of excess market and Jensen’s alpha performance 
 
Note: Figure 4.6 illustrates the percentage of time that the actual t-statistics for excess market and Jensen’s alpha 
outperformance fall within the indicated range in comparison with a normal distribution. The analysis calculates 
excess market and Jensen’s alpha outperformance for all industries across all business cycle stages with Equation 4.3 
and Equation 4.4 for a total of 240 (5 x 48) corresponding t-statistics for each performance measure. 
  
0%
5%
10%
15%
20%
25%
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58
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 -1
.96
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96
 to
 -1
.65
-1.
65
 to 
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o -
0.5
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5 to
 0
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 0.5
0.5
 to 
1
1 t
o 1
.65
1.6
5 to
 1.9
6
1.9
6 to
 2.5
8
2.5
8 to
 3.2
9
> 3
.29
Fr
equ
enc
y
Statistical Significance of Market Alphas
Actual distribution of excess market t-sta tistics Expected distribution of excess market t-sta tistics assuming a  normal distribution
0%
5%
10%
15%
20%
25%
< -
3.2
9
-3.
29
 to 
-2.
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-2.
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 to 
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 to 
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 to 
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-0.
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 0.5
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 to
 1
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 1.6
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 1.9
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 2.5
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 3.2
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Fr
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Statistical Significance of Jensen's Alphas
Actual distribution of Jensen's a lpha t-statistics Expected distribution of Jensen's a lpha t-statistics assuming a  normal distribution
 184 
Panel A of Table 4.17 reports industries by the business-cycle stage identified as 
providing jointly systematic excess market and Jensen’s alpha performance, over the 
nine business cycles from January 1948 to March 2001. The table reports excess market 
and Jensen’s alpha performance as annual rates, where bold indicates 10 percent 
statistical significance using White (1980) heteroskedasticity consistent t-statistics. 
Column 4 of Panel A reports the percentage of months an industry realizes positive 
market performance. The analysis tests the percentage of months an industry realizes 
excess performance against a 50/50 random probability, where bold again indicates 10 
percent statistical significance. There are three industries included in each business-
cycle stage, with the exception of five industries in early recession, which meet the 
criterion for alternative sector rotation. Notably, seven of those industries do correspond 
with popular belief on anticipated industry performance. For example, the gas and 
electric, and communication industries have proven consistently to provide early 
recession performance, both in the base-case analysis and robustness tests. 
Panel B of Table 4.17 reports performance results for the alternative sector rotation 
strategy. The out-of-sample period covers the NBER business cycle that runs from 
January 2001 to December 2007. Panel B reports mean returns, standard deviations, and 
Jensen’s alphas as annual rates, along with single-index betas and Sharpe ratios. Bold 
indicates Jensen’s alpha performance statistically significant at 10 percent using White 
(1980) heteroskedasticity consistent t-statistics. The alternative sector rotation industries 
realize in-sample excess market performance approximately 62 percent of all months 
for the selected stages. Nonetheless, the alternative strategy, which showed such in-
sample promise, actually realizes a negative 4.4 percent annual Jensen’s alpha out-of-
sample, although this is statistically indistinguishable from zero. Transaction costs 
further diminish strategy performance. Notably, the market-timing Jensen’s alpha also 
drops from 2.5 percent for the full period to a negative 0.4 percent, although here again 
insignificantly so. One could argue that the 2001–2007 business cycle is not 
representative of typical business cycles and thus is an unfair test of any sector rotation 
strategy. Alternatively, one might conclude that industry fundamentals vary from one 
business cycle to the next, making it impracticable to implement profitable sector 
rotation. Evidence of unsystematic sector performance across business cycles suggests 
the latter. 
 185 
Table 4.17 Alternative sector rotation strategy 
 
Notes: Panel A of Table 4.17 reports industries that over the period from January 1948 to March 2001 provided 
jointly statistically significant excess market returns and Jensen’s alphas during the indicated business-cycle stage. 
The table reports excess market returns and Jensen’s alphas as annualized rates and indicate White (1980) 
heteroskedasticity consistent t-statistics significant at 10 percent in bold. Column 4 reports the percentage of months 
realized industry returns exceed the market. The analysis tests for the percentage of months an industry provides 
realized outperformance against a random 50/50 chance and indicates 10 percent statistical significance in bold. Panel 
B of Table 4.17 reports mean returns, standard deviations, single-index betas, Sharpe ratios, and Jensen’s alphas for 
an out-of-sample performance of an alternative sector rotation strategy based on Panel A. Panel B reports mean 
returns, standard deviations, and Jensen’s alphas as annual rates. Bold indicates Jensen’s alpha performance 
significant at 10 percent using White (1980) heteroskedasticity consistent t-statistics. 
Panel A:
excess % month Jensen's 
Period 1948:01 - 2001:03 Sector/Industry market positive alpha
Early Expansion - Stage I Transportation 0.073 0.568 0.060
Automobiles & Trucks 0.081 0.580 0.088
Trading 0.048 0.574 0.042
Middle Expansion - Stage II Candy & Soda 0.115 0.638 0.128
Pharmaceutical 0.088 0.582 0.088
Printing & Publishing 0.060 0.548 0.057
Late Expansion - Stage III Medical Equipment 0.061 0.568 0.062
Coal 0.150 0.526 0.150
Construction 0.088 0.553 0.087
Early Recession - Stage IV Shipping Containers 0.089 0.653 0.079
Chemicals 0.088 0.571 0.079
Food Products 0.214 0.776 0.171
Gas & Electric 0.246 0.776 0.177
Communication 0.186 0.612 0.085
Late Recession - Stage V Personal Services 0.154 0.723 0.157
Apparel 0.190 0.617 0.198
Printing & Publishing 0.173 0.702 0.162
Panel B:
Period 2001:04 - 2007:12 Sharpe  Jensen's  
Strategy Performance mean std. dev. beta ratio alpha
Market 7.44% 13.10% 1.00 0.10
0% round-trip transaction costs
Alternative Sector Rotation 3.03% 17.64% 1.07 0.00 -4.44%
Market-timing 6.77% 12.71% 0.94 0.09 -0.36%
0.5% round-trip transaction costs
Alternative Sector Rotation 2.73% 17.57% 1.07 0.00 -4.70%
Market-timing 6.69% 12.71% 0.94 0.09 -0.44%
1.0% round-trip transaction costs
Alternative Sector Rotation 2.42% 17.51% 1.06 -0.01 -4.96%
Market-timing 6.61% 12.72% 0.94 0.08 -0.52%
1.5% round-trip transaction cost
Alternative Sector Rotation 2.12% 17.46% 1.06 -0.01 -5.22%
Market-timing 6.54% 12.73% 0.95 0.08 -0.60%
Performance
 186 
4.8 Conclusion 
Despite thorough empirical tests, there is scant evidence that conventional sector 
rotation across business cycles generates systematic excess returns. The base-case 
analysis assumes that sector rotation investors perfectly time business cycles and rotate 
sectors in accordance with popular belief on sector performance. Even then, sector 
rotation generates, at best, 2.3 percent annual outperformance. Performance quickly 
diminishes with the introduction of transaction costs or business cycle mistiming. In 
comparison, a similar investor, with perfect market timing ability, would realize 2.5 
percent annual outperformance by simply switching to cash during early recession. 
Lastly, the analysis generalizes the base case to allow for all possible sector rotation 
variations. The analysis explores whether any industry provides systematic performance 
across any business-cycle stage. The general results again provide limited evidence of 
systematic industry performance over business cycles. A group of industries does 
generate in-sample systematic outperformance, which is marginally different from that 
expected to occur randomly, absent any systematic outperformance. However, an out-
of-sample sector rotation strategy, based on those same industries, fails to generate 
significant outperformance. The results suggest that no variation of sector rotation 
provides systematic outperformance, questioning the popular belief that timing sector 
investments with business cycles generate excess returns. The results do not necessarily 
preclude investors from profiting through sector rotation. Different investments in 
sector and industry funds, beyond the scope of this study, may outperform the market. 
The results simply show that sectors fail to provide systematic performance across the 
business cycle and question the viability of popular sector rotation. 
  
 187 
Chapter 5  Conclusions 
5.1 Summary of Contributions 
The efficient market hypothesis is the cornerstone of modern finance. In perfectly 
efficient markets, stock prices fully and instantaneously reflect all available information. 
Theoretically, investors should be unable to earn excess returns trading on systematic 
cycles in the stock market, especially after deducting transaction costs. While the 
efficient market hypothesis remains the principal tenet of finance, evidence of periodic 
stock market cycles and speculative investor sentiment appear to contradict theory. 
Behavioral finance provides an alternative financial theory that describes inefficient 
markets, which allows for market cycles and strategies that earn excess returns. The 
Hong and Stein (1999) model portrays investors who are boundedly rational, due to 
their inability to fully process fundamental news. Other theoretical studies, such as 
Barberis and Shleifer (2003) and Peng and Xiong (2006), argue that limited investor 
attention leads to categorized learning and style investing. Importantly, Kumar and Lee 
(2006) confirm that the collective trades of uninformed investors ― those who trade on 
sentiment rather than fundamental value ― result in mispricing and return 
predictability. Research by Baker and Wurgler (2006), among others, provides 
empirical evidence that investor sentiment leads to speculative mispricing, especially in 
particular styles. Behavioral finance studies support the occurrence of stock market 
cycles and sentiment-driven mispricing, topics this dissertation explores. 
 Specifically, this dissertation examines industry returns for evidence of cyclical 
performance. Evidence of cyclical industry performance is interesting from two 
perspectives. Firstly, stock market cycles, unrelated to fundamental value, challenge the 
efficient market hypothesis. Nonetheless, an evolving literature, coupled with anecdotal 
evidence, indicates that stock prices, at times, do not fully reflect fundamental value. 
Investigating industry performance provides additional insight into stock market cycles 
previously documented. Secondly, popular belief suggests that predictable industry 
cycles facilitate profitable investment strategies. The capital that such strategies attract 
makes the topic interesting from a practical perspective. Taken together, both 
perspectives suggest the need for an investigation of whether there are systematic cycles 
in industry performance. Each of the three empirical studies investigates industry 
 188 
returns for evidence of different cycles. The three industry cycles investigated are 
investor-sentiment cycles, political cycles, and business cycles. The following sub-
sections summarize each of the empirical studies. The last section discusses limitations, 
and outlines an intended agenda for future research. 
5.1.1 Investor sentiment and industry returns 
The first study investigates the relationship between investor sentiment and industry 
returns. The efficient market hypothesis makes no provision for investor sentiment in 
asset pricing. However, anecdotal evidence periodically suggests that investor sentiment 
drives stock market performance. The financial media also widely report on investor-
sentiment measures as predictors of market direction. Particular industries, outlined in 
Baker and Wurgler (2006), appear especially sensitive to speculative investor sentiment. 
Empirical research provides some evidence that investor sentiment does predict stock 
market returns. Research shows that investor sentiment positively predicts short-term 
market overreaction and long-term market reversals. Evidence that investor sentiment 
predicts stock returns presents another challenge to market efficiency, which the first 
study endeavors to understand through industry analysis. The study examines investor 
sentiment predictability of industry performance and its practical application in industry 
rotation. 
The first study documents investor sentiment predictability of industry returns. 
However, timing industry investments with sentiment generates only marginal 
outperformance. The study investigates the interaction between industry performance 
and three investor-sentiment measures, over the sample period 1987–2007. Confirming 
other empirical research, it finds that investor sentiment positively predicts short-term 
and negatively predicts long-term market returns. Predictability is more evident in 
equal-weighted indices, given the greater influence of small-cap stocks. Investor 
sentiment similarly has a predictable effect on industry performance. The study 
documents universal predictability of short-term industry performance. However, long-
term predictability is limited over the full sample. Long-term predictability is greater for 
bearish sentiment than it is for bullish sentiment. Study one finds that difficult-to-value 
industries are no more susceptible to speculative investor sentiment than others are. 
Investor sentiment predictability of industry returns does not readily translate into a 
 189 
profitable investment strategy, due to high strategy turnover. Study one documents 3 to 
6 percent annual outperformance to strategies that use investor sentiment to rotate 
industries, which transaction costs would largely consume. Overall, the first study 
concludes that the effect of investor sentiment is market-wide rather than industry-
specific. Industry returns thus provide no additional explanation for investor sentiment 
predictability of stock returns. 
5.1.2 Political cycles in U.S. industry returns 
Study two investigates industry returns for evidence of two well-known presidential 
election cycles. Prior empirical studies document stock market cycles related to a 
president’s political affiliation (presidential election cycle) and the years of a four-year 
presidential term (quadrennial cycle). Generally, research shows that the market 
performs best under Democratic presidents and in the last two years of any presidential 
term. Research, thus, contradicts popular belief that the stock market performs best 
under Republican presidents. There is also widespread belief that certain industries 
systematically perform best under Republican presidents and other industries perform 
best under Democratic presidents. Predictable political stock market cycles challenge 
market efficiency and remain an unanswered puzzle in the financial literature. The 
second study investigates political cycles in industry returns for two reasons: to provide 
a possible explanation for political stock market cycles, and to test the popular belief 
that political cycles drive industry performance. 
The second study documents political stock market cycles in the market and in 
unadjusted industry returns. The sample covers 21 presidential election cycles from 
1926 to 2006. Confirming prior studies, such as Booth and Booth (2003) and Santa-
Clara and Valkanov (2003), study two provides evidence of political cycles in the 
general market. Stock market returns are 8.6 percent higher under Democratic 
presidents and 10 percent higher in the second half of any presidential term. Unadjusted 
industry returns exhibit similar political cycles. However, evidence of presidential and 
quadrennial cycles mostly dissipates in industry returns after a Fama and French three-
factor risk correction. Size and value risk factors thus largely explain political cycles in 
industry performance. The second study also tests differences in expected industry 
performance, using an event study to analyse the closely contested 2004 presidential 
 190 
election. The event study analysis groups industries by political orientation, political 
contributions, and analyst recommendations. Statistically insignificant excess returns, 
for all groups, indicate that election outcomes provide a noisy signal of differences in 
expected industry performance. Contrary to popular belief, study two provides no 
evidence of a relationship between industry performance and political cycles. The 
second study concludes that political cycles remain a market-wide puzzle, unexplained 
by industry performance. 
5.1.3 Sector rotation across business cycles 
The third study investigates the two fundamental assumptions of a popular investment 
strategy known as sector rotation: that industries provide systematic business-cycle 
performance, and that sector rotation generates excess risk-adjusted performance. 
Profitable sector rotation further requires perfectly timing industry investments with 
business-cycle stages. To overcome that obstacle, study three makes the simplifying 
assumption of an investor who has perfect business-cycle foresight. Study three initially 
examines a common variation of sector rotation, which rotates specific industry 
investments over NBER-delineated business-cycle stages. Extended analysis 
investigates the performance of all industries across all business-cycle stages, as a 
generalization that covers any possible variation of sector rotation. The third study 
includes additional robustness tests in order to account for alternative business-cycle 
stage delineations; sector and industry classifications; risk-correction measures, and 
strategy implementations. 
The third study documents limited evidence that industry performance systematically 
varies over business cycles in a way that facilitates profitable sector rotation strategies. 
The full sample covers 10 NBER business cycles from 1948 to 2007. A sector rotation 
strategy that follows popular guidance on industry performance generates at best 2.3 
percent annual outperformance. Performance does not improve rotating industries one 
to three months before or after business-cycle turning points. Moreover, imperfectly 
timing business cycles and deducting transaction costs would quickly eliminate 
outperformance. The third study shows that an investor with business-cycle foresight 
would do better by simply switching from the market index to cash in recessions. More 
generally, there is only limited evidence that any industry systematically performs better 
 191 
during any business-cycle stage. The unsystematic performance of industries over 
business-cycle stages questions the popular belief that sector rotation generates 
outperformance, while also confirming market efficiency. 
5.2 Limitations and Future Research Agenda 
This section discusses limitations and an intended agenda for future research. The 
dissertation examines industry performance related to sentiment cycles, political cycles, 
and business cycles. While market efficiency maintains that stock returns follow a 
random walk, widespread belief persists that there are predictable cyclical patterns in 
industry returns. Shiller (2003) discusses the importance of better understanding such 
irrational expectations, which ultimately result in speculative asset pricing. To that end, 
there are aspects of industry performance that deserve further exploration. 
The first study documents that the effect of investor sentiment on market returns 
extends, almost universally, to industry performance. Moreover, industry characteristics 
fail to explain investor sentiment predictability of industry returns. This result runs 
contrary to market studies, which show speculative mispricing related to certain stocks’ 
characteristics. As such, future research will seek to understand better the relationship 
between industry mispricing and investor sentiment. An intended approach is to 
disentangle industry sentiment from market-wide investor sentiment. This approach will 
require the construction of an industry-specific investor-sentiment measure, similar to 
the Baker and Wurgler (2006) sentiment index. A direct industry sentiment measure, 
thus, could better explain industry return predictability and industry boom-and-bust 
cycles. 
Political cycles in the U.S. stock market remain an unexplained puzzle, in 
contradiction of the efficient market hypothesis. The first essay shows that systematic 
sources of risk largely explain presidential cycles in U.S. industry returns, leaving 
political stock market cycles a puzzle. There are at least two alternative explanations of 
political stock market cycles. In the U.S., the Congress controls government 
expenditures. A later study will further examine industry performance related to the 
interaction between political control of Congress and the presidency. The question also 
remains of causality – does politics determine stock market cycles, or the reverse? For 
instance, influential industries could determine presidential election outcomes. Santa-
 192 
Clara and Valkanov (2003) additionally recognize the possibility that macro-economic 
conditions drive political cycles. Future research will thus further examine these 
possible explanations of political stock market cycles. 
The third essay documents no evidence of systematic industry performance 
corresponding with business cycles. A remaining question is whether rotating across 
alternative investment styles (e.g. size, growth, value, and international) provides 
predictable business-cycle outperformance. Lucas, van Dijk, and Kloek (2002) and 
Ammann and Verhofen (2006) observe that the returns of certain investment styles do 
exhibit pro-cyclical patterns. Næs, Skjeltorp, and Ødegaard (2011) also observe that 
time-variant investor preferences correspond with business cycles. Their study 
particularly documents a “flight to quality” as business cycle conditions worsen, 
affecting market liquidity and valuations. During an economic downturn, for instance, 
investor participation in small-cap stocks diminishes. Additionally, Vardharaj and 
Fabozzi (2007) show that time-variant asset allocations account for 90 percent of 
investment returns. Consequently, differences in cross-sectional style performance 
across business cycles potentially result in profitable investment allocation strategies. A 
future study explores this possibility. 
A further extension of study three will examine the relationship between industry 
values and credit cycles. Research shows that credit cycles are distinct from traditional 
business cycles. Bordo and Haubrich (2009) find that phases of credit and business 
cycles overlap only about 34.4 percent of the time. Moreover, Claessens, Kose, and 
Terrones (2008) show that credit cycles generally lead business cycles. Their study also 
shows that economic contractions are more severe when traditional business cycles 
coincide with credit cycles. Minsky (1992) describes credit availability as the driver of 
financial market boom-and-bust cycles. From a Minsky perspective, cycles of credit 
expansion and contraction underlie business cycles and financial market fragility. 
Minsky delineates stages of expanding credit, diminishing risk aversion, and speculation 
that potentially lead to industry valuation cycles and subsequent mispricing. An analysis 
of the interaction between Minsky credit-cycle stages and industry performance may 
provide an alternative explanation of popular rotation strategies. 
 193 
Finding spurious patterns in market returns does not provide conclusive evidence of 
violations of the efficient market hypothesis. A limitless number of variables are 
available that can potentially describe statistical patterns in the data – even where none 
exist. The extant literature documents a number of apparently systematic patterns, 
which might simply represent artifacts of the data. A true test of cyclical patterns is 
whether trading on them generates systematic outperformance. This thesis tests industry 
returns for evidence of sentiment cycles, political cycles, and business cycles. In all 
instances, unsystematic industry performance provides no evidence that investors can 
generate excess returns by timing their investments with these cycles. Rational 
explanations, such as corrections for well-known sources of risk, largely explain any 
apparent evidence of systematic industry performance. Additionally, trading costs 
further explain any excess returns that timing industry investments with the examined 
cycles accrue. It appears that if investors do profit from cyclical trading strategies, they 
are trading on noise rather than systematic industry performance. Ultimately, the results 
of this thesis are not encouraging for those who seek to exploit apparent deviations from 
market efficiency. To that extent, industry performance appears transactionally efficient, 
despite an extensive literature that documents cyclical market performance. 
Nonetheless, the absence of systematic industry cycles leaves unanswered the puzzle of 
cycles documented in the general market. However, perhaps as Black (1986) suggests, 
such apparent deviations from market efficiency merely represent unexploitable noise, 
from which traders are unable to economically benefit. If so, one can conclude that 
markets are efficient, as traditional financial theory prescribes.  
 
 194 
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