Essays on individual stock returns predictability : a thesis presented in fulfillment of the requirement for the degree of Doctor of Philosophy in Finance at Massey University, Albany, New Zealand
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Date
2022
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Massey University
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Abstract
This dissertation considers different aspects of individual stock predictability.
The first essay shows that the previously documented predictability of macroeconomic and technical variables for market returns is also evident in individual stock returns. Technical variables generate better predictability on firms with high limits to arbitrage (small, illiquid, volatile firms), while macroeconomic variables better predict firms with low limits to arbitrage. Technical predictors show a stronger predictive power for high limits to arbitrage firms across the business cycle, whereas macroeconomic variables capture more predictive information for firms with low limits to arbitrage during recessions.
The second essay shows that 14 widely documented technical indicators explain cross-sectional expected returns. The technical indicators have lower estimation errors than the three-factor Fama-French model and historical mean. The long-short portfolios based on cross-sectional estimated returns consistently generate substantial profits across the entire period. The well-known cross-sectional expected return determinants, including momentum, size, book-to-market, investment, and profitability, do not explain the explanatory power of technical indicators. Our findings suggest that technical indicators play an important role in determining the variation in cross-sectional expected returns in addition to the five-factor model.
In the third essay, we use firm characteristics to estimate the enduring momentum probabilities for past winners (losers) to continue to be future winners (losers). The enduring momentum probability is significantly related to stock return persistence and explains cross-sectional expected returns. In addition, it contains different information from momentum signals. Combining the two pieces of information generates an enduring momentum strategy that produces a 2.19% return per month, almost doubling the momentum return. Factors that drive the price momentum strategy, such as seasonality, limit to arbitrage, and transaction costs, do not fully capture the performance of the enduring momentum strategy.
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Stocks, Rate of return, Forecasting, Mathematical models