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    Real-time GDP nowcasting in New Zealand : an ensemble machine learning approach : a thesis presented for the degree of Master of Philosophy, School of Natural and Computational Sciences, Massey University, New Zealand
    (Massey University, 2019) Fan, JinJin
    Gross Domestic Product (GDP) measures the monetary value of all final goods and services that are produced in a region during a period of time. For most countries, GDP is released a limited number of times a year and often with a lag. Understanding the current economic situation, instead of figures quarters ago, is of vital importance for both policy and private entrepreneurs. It is crucial to create a live GDP predictor that could Nowcast current GDP growth rate in the period of government statistics release delay. The Econometric approach for GDP Nowcasting has dominated the forecasting area for many years. However, most of the traditional econometric models could only incorporate a small handful of variables with a linear model structure, which could not meet the requirement of the “big data” era for a better model prediction ability with a large amount of unbalanced variables. With the improvement of computation ability and the increment of high frequency variables, data-driven approaches like Machine Learning Methods have been applied into Nowcasting area. It does not only show a stronger forecasting ability in handling large number of predictors but also present a superior robustness for non-linear data structure. In this research, an Ensemble Method constructed by several Machine Learning Methods have been generated to provide more timely available GDP figures in the period of government statistics release delay. Having integrated an input dataset with data from multiple data sources such as public statistical websites, Reserve Bank of NZ and Stats NZ, our cooperators New Zealand Transport Agency (NZTA) and PayMark, this study is conducted by first applying different Machine Learning methods such as Lightgbm, Xgboost, Support Vector Machine, K- Nearest Neighbors, Ridge Regression, Lasso, Adaboost models. Then these algorithms are combined to generate an Ensemble Model with the assistance of an averaging method, which weights each model individually based on its historical prediction accuracy. The result of the final Ensemble Model is compared with the most commonly used benchmark ARIMA model and Random Walk model in terms of Mean Square Error (MSE) and Median Absolute Error(MAE) value. Statistical tests, such as Friedman Test and Wilcoxon Signed-Rank Test, are employed to check the significance of model superiority. The results indicate that the Ensemble Model significantly outperforms individual Machine Learning algorithm and Random Walk model in forecasting accuracy. When compared with the ARIMA model, it shows slightly better prediction ability with more fore-sights especially in a fluctuating environment.
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    Cluster analysis and firm patterns of earnings persistence : a new approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Manawatū, New Zealand
    (Massey University, 2019) Tran, Son Duong
    The development of a method to appropriately address the problem of heterogeneous-group specific coefficients (HGSC) is of paramount importance for any studies where there are concerns of HGSC. Accordingly, the goal of this thesis is to investigate a solution to the prevalent problem of HGSC within the context of the finance discipline. Specifically, this thesis introduces a novel clustering procedure called regression oriented-weighted K- means clustering (ROWK). This new method employs the regression mean absolute residuals (MAR) to inform the cluster analysis identification of optimal feature weights. The performance of ROWK clustering is examined via both simulated and real data. Simulation results show significant improvements from the adoption of ROWK relative to K-means clustering and weighted K-means clustering through three channels. Specifically, through the examination of three case studies, this thesis finds that ROWK places more (less) weight on more (less) relevant features; reduces the influence of multicollinearity by reducing the weights of irrelevant features which are highly correlated with relevant features; and captures relevance not only by its contribution to cluster recognition but also by regression estimation. The thesis further examines the performance of ROWK clustering using real data for earnings persistence models. ROWK outperforms other standard techniques in the sense of correctly identifying the underlying clusters on earnings persistence models. The thesis also documents that analysts’ forecasts only partially incorporate the information from cluster patterns in the short run, while ignoring impacts of these patterns on long-term future earnings. As a result, conditioning on such information allows the identification of reliable and economically important patterns in analyst forecast errors.
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    Trans-Tasman transmission of fiscal shocks : a thesis submitted in partial fulfilment of the requirements for the Master of Business Studies (Financial Economics) at Massey University, Albany
    (Massey University, 2006) Bodle, Michael
    This paper investigates how shocks to government spending and income taxes in Australia affects both Australia and New Zealand economies and looks at the channels through which these effects are transmitted from one economy to the other. A semi-structural vector auto regressive (VAR) approach is used to analyse quarterly data from the period: 1974:3 – 2005:4. The empirical results show that a shock to Australian income tax revenues leads to a decrease in both Australian and New Zealand output, and a shock to Australian government consumption leads to an increase in both Australian and New Zealand output. The impact of government expenditure shocks is transmitted through the interest rate channel only. The empirical results also suggest that the impact of an income tax shock is transmitted through the interest rate channel, which dominates the effect of the exchange rate channel.
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    A study into the consequences of using the Monetary Conditions Index as an operational target for monetary policy in New Zealand : a thesis submitted in partial fulfillment of the requirements for the degree of Master of Applied Economics at Massey University, New Zealand
    (Massey University, 2000) Loomes, Robin Andrew
    The Reserve Bank of New Zealand (RBNZ) used a Monetary Conditions Index (MCI) as an operational guide for monetary policy from 30 June 1997 until 16 March 1999. This thesis uses four different methodologies to determine how this affected the implementation of monetary policy. The first is a survey that investigates the impact of the MCI regime on the way financial market participants viewed the RBNZ's policy stance. The second methodology consists of a series of roiling 10-week regressions that examines the relationship between short-term interest rates and the exchange rate. The third methodology is the autoregressive-distributed lag procedure, which explores the links between the RBNZ's policy actions, the MCI and its components, and external influences as represented by the United States 90-day bank bill rate. Finally, additional information about these relationships is obtained from block Granger causality tests, forecast error variance decompositions and impulse-response functions derived from a VAR framework. This study draws two major conclusions from the results. First, the MCI regime was responsible for an inverse relationship, which developed between the two components of the MCI and lasted from June 1997 until November 1998. This deepened the recession in 1998 by raising short-term interest rates when the currency depreciated after mid-1997. Second, the MCI regime did not significantly change the way the RBNZ's policy instruments impacted on the MCI or its components.
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    Sharemarket performance and the New Zealand dollar : inside the relationships : a thesis presented in partial fulfilment of the requirements for the degree of a Master of Applied Economics, Massey University, Palmerston North, New Zealand
    (Massey University, 2006) Pech, Andrew
    New Zealand is often described as a small open economy with substantial foreign ownership of its assets. The economy is therefore sensitive to exchange rate movements and the sharemarket being the barometer of economic activities should be no exception. Further, exchange rates may also be endogenous to sharemarket fluctuations. This thesis analyses the relationship between the value of the New Zealand dollar vis a vis the currencies of its five largest trading partners and the New Zealand sharemarket performance between 1999 and mid-2005 using the vector autoregression (VAR) and vector error correction model (VECM) approaches. Findings from the research suggest the New Zealand sharemarket is robust to currency fluctuations in both the short- and long- term. The only exception to this is the New Zealand dollar-Australian dollar exchange rate (NZD/AUD), which has a negative short term effect on the sharemarket. The NZD/AUD is also the only exchange rate to depreciate following a positive shock to the sharemarket.
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    Explaining the cross-country variation in fiscal multipliers : a Bayesian approach : a thesis presented in partial fulfillment of the requirements for the degree of Master of Business Studies in Financial Economics at Massey University, Albany, New Zealand
    (Massey University, 2007) Purushothman, Nanda Kishora
    The effectiveness of fiscal policy is subject to crowding out. For nearly thirty years of annual economic data, we find that the crowding out of fiscal policy occurs through interest rate and exchange rate channels. The three most important determinants affecting the size and sign of fiscal multipliers during recessions worldwide are: (i) exchange rate regime, (ii) monetary policy, and (iii) current account balance. We find statistically significant results that these accompanying policies are the most influential sources of the cross-country variation in fiscal multipliers. Similarly, using an OECD dataset examining both economic expansions and recessions, we find that the three most statistically significant variables affecting fiscal multipliers in this case are: (i) exchange rate stance, (ii) private investment, and (iii) monetary policy. We find that the coefficient of the private investment variable is significantly negative, which is in line with the theoretical predictions. This finding is consistent with the hypothesis that expansionary government spending financed by debt crowds out private investment through rising interest rates.
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    Failure prediction of Chinese A-share listed companies : comparisons using logistic regression model and neural network analysis : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Finance at Massey University, Palmerston North, New Zealand
    (Massey University, 2004) Lan, Hua Yong
    This study compares the relative prediction accuracy of corporate failure between two prediction methods –logistic regression model and neural network analysis– based on a sample of 3598 observations and companies data obtained from the Chinese A- Share market during the period 1991 to 2002. Seven criteria have been set up to define failure according to attributes of Chinese listed companies. Using forty financial ratios and seven misclassification cost ratios of Type I and Type II error, two models achieve ranges of minimal misclassification cost at optimal cut-off points for two years prior to business failure; The logistic regression model is slightly superior to neural network analysis. Compared with random prediction, both models are efficient. In addition, the study points out that Total Asset Turnover (TATR), Cash Ratio (CASR), Earning per Share (EPS), Total Debt to Total Asset (TDTA), Return on Assets (ROA) and the natual log of Total Market Value (MVLN) could be significant financial indictors of corporate failure. Results of the study have important implications in credit evaluation, internal risk control and capital market investment guidelines.
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    Shewhart methodology for modelling financial series : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
    (Massey University, 2017) Premarathna, Liyana Pathiranaralalage Nadeeka Dilrukshi
    Quality management techniques are widely used in industrial applications for monitoring observable process variation. Among them, the scientific notion of Shewhart principles is vital for understating variations in any type of process or service. This study extensively investigates and demonstrates Shewhart methodology for financial data. Extremely heavy tails noted in the empirical distribution of stock returns led to the development of new parametric probability distributions for pricing assets and forecasting market risk. Standard asset pricing models have also extended to account the first four (excess) moments in return distributions. These approaches remain complex, but yet they are inadequate for capturing extreme volatility caused by infrequent market events. It is well known that the security markets are always subjected to a certain amount of variability caused by noise-traders and other frictional price changes. Unforeseen events which are happening in the world may lead to huge market losses. This research shows that Shewhart methodology for partitioning data into common and special cause variations adds value to modelling stock returns. Applicability of the proposed method is discussed using several scenarios occurring in an industrial process and a financial market. A set of new propositions based on Shewhart methodology is formed for finer description of the statistical properties in stock returns. Research issues which are related to the first four moments, co-moments and autocorrelation in stock returns are identified. New statistical tools such as difference control charts, odd-even analysis and estimates for co-moments are proposed to investigate the new propositions and research issues. Finally, several risk measures are proposed, and considered with respect to investor’s preferences. The research issues are investigated using partitioned data from S&P 500 stocks and the findings show that inmost of the scenarios, contradictory conclusions were made as a result of special cause variations. A modelling approach based on common and special cause variations is therefore expected to lead appropriate asset pricing and portfolio management. New statistical tools proposed in this study can be used to other time series data; a new R-package called QCCTS (Quality Control Charts for Time Series) is developed for this purpose.
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    An econometric analysis of the determinants of growth in the Kingdom of Tonga, 1970-1998 : a research thesis submitted in partial requirement for the degree of Master of Applied and International Economics at Massey University
    (Massey University, 1999) Faletau, Siosaia Tupou
    The importance of determining the factors that contribute to economic growth is vital in the case of Tonga because of the benefits and advantages it provides for the people and their future development. The main objective of this study is to analyse and investigate empirically the macroeconomic factors that promote economic growth and development in Tonga. Economic theories and various studies have presented the variables that may affect growth. These include investment (domestic and foreign), labour force, exports and imports, fiscal policies, tourism receipts, private remittances, foreign aid and its various components. Foreign resources such as aid and private remittances play an important role in the development of small island economies and Tonga's heavy reliance on these factors may also explain their contribution to growth. The study uses a neoclassical production function to examine the relationships between economic growth in Tonga and the proposed determinants listed above. The cointegration method of Auto-Regressive Distributed Lag is utilised in the analysis. The empirical evidence indicates that factors making a positive contribution to economic growth in Tonga are the growth in exports, tourism receipts, openness to trade, government consumption expenditure, bilateral aid, grant aid and imports. The loan aid, multilateral aid, technical co-operation grants and private remittances, while significant in most cases, show a decline over time. Natural disasters and external market shocks have a strong adverse effect on Tonga's growth rate. The issue of macroeconomic management is stressed in this study as the key role to be played by the government in order for the available resources to be allocated to the productive sectors of the economy. This can be undertaken through setting stable macroeconomic environment, introducing and maintaining growth-oriented policies and structural reforms in some of the key sectors of the economy. Research should be concentrated on high value niche products and promoting technological development to support the diversification in the export and tourism sectors. Measures should also be adopted to monitor the effectiveness of utilising foreign aid projects, as current aid flows show a decline.
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    Essays on exchange rates : a dissertation submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, School of Economics and Finance, Massey University
    (Massey University, 2016) Kleinbrod, Vincent
    This dissertation presents three essays on exchange rates. The reported work builds on the market microstructure approach to exchange rate determination and extends this approach to modelling and forecasting multivariate exchange rate movements, and to a multi-currency trading application. The first study investigates the role of order flow in explaining joint movements of exchange rate returns, thereby building an original bridge between exchange rate co-movement and the market microstructure literature. We document that absolute order flow differentials have a significant negative effect on future joint currency movements at intraday frequencies. The analysis also shows that other intraday variables, such as the bid–ask spread, have no explanatory power for the co-movements after the absolute order flow differential is accounted for, thereby confirming the robustness of order flow as the driving force for exchange rate correlation. Further analysis demonstrates that absolute order flows also affect conditional variance dynamics. The second study adds to the findings of the first study. It evaluates the information content of order flow for accurate predictions of exchange rate co-movement. In line with the first study, we find that order flow information substantially enhances the accuracy of covariance forecasts. Moreover, the interest rate differential has a limited role in explaining and predicting correlation dynamics once the order flow differential is accounted for. The study concludes by showing the economic value of the order-flow-based covariance predictions, namely the value of order flow information for covariance predictions beyond return predictions. The third study focuses on the practical relevance of order flow information in foreign exchange trading. Given the dominance of technical trading among forex professionals, the study evaluates the value of order flow information for technical traders. Our initial investigation questions the accuracy of trading signals if these are derived directly from order flow. We conjecture that the reason for this is that order flow should first be used to generate exchange rate predictions, which can then be used to derive profitable trading signals. We examine this conjecture empirically, and the affirmative results highlight the value of order-flow-based return predictions for technical analysis. Further, we propose a multivariate trading strategy to boost the benefits of using order flow in technical analysis, which is shown to be a highly profitable.