published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

Liu Zheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 69-72). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Contributions --- p.2 / Chapter 1.2 --- Dissertation Organization --- p.3 / Chapter 2 --- Problem Statement and Background Study --- p.4 / Chapter 2.1 --- Problem Statement --- p.4 / Chapter 2.2 --- Background Study --- p.6 / Chapter 2.2.1 --- Overview of Pattern Discovery Methods --- p.7 / Chapter 2.2.2 --- Applications --- p.9 / Chapter 3 --- Ball Discovery Algorithms --- p.13 / Chapter 3.1 --- Brute-force Method for Ball Discovery --- p.13 / Chapter 3.2 --- Ball Discovery with Small Point Sets --- p.15 / Chapter 3.2.1 --- Pruning the Search Space Using RP-tree --- p.15 / Chapter 3.2.2 --- CB-tree - Collection of Balls in a Compact and Complete Form --- p.22 / Chapter 3.2.3 --- Algorithm of Finding Balls Using RP-tree and CB-tree --- p.31 / Chapter 3.3 --- Ball Discovery in Large Point Sets --- p.31 / Chapter 3.3.1 --- Candidate Sets of Balls --- p.31 / Chapter 3.3.2 --- A Divide-and-Conquer Algorithm --- p.35 / Chapter 3.4 --- Heuristic Greedy Algorithms for Ball Discovery --- p.37 / Chapter 3.4.1 --- A Heuristic Greedy Algorithm --- p.37 / Chapter 3.4.2 --- Another Heuristic Greedy Algorithm --- p.38 / Chapter 4 --- Evaluations --- p.40 / Chapter 5 --- Discussion --- p.59 / Chapter 5.1 --- Order and Index the Points --- p.59 / Chapter 5.2 --- Incremental Points Update --- p.59 / Chapter 5.3 --- Smallest Enclosed Ball Algorithm --- p.60 / Chapter 6 --- Conclusion and Future Research --- p.61 / Chapter A --- Appendix --- p.63 / Chapter A.1 --- Fundamental Algorithms --- p.63 / Chapter A.1.1 --- Computing Smallest Enclosed Ball of a Point Set in Euclidean Space --- p.63 / Chapter A.1.2 --- Finding All Cliques of an Undirected Graph --- p.65 / Chapter A.2 --- An Example of a Small Data Set --- p.66 / Bibliography --- p.69

published_or_final_version / Business / Master / Master of Philosophy

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016. / This paper builds upon Blanchard's (1981) model of asset prices, and provides an empirical evidence for good news cases (GNC) and/or bad news cases (BNC) as de ned in Blanchard's paper. We update Blanchard's model by introducing Taylor's rule of monetary policy and explicitly incorporate income distribution in a small, open economy. The ndings indicate that, the labour share is a strong and signi cant variable that should be considered in asset pricing models. The real exchange rate plays a signi cant role in the determination of asset prices in most of the selected countries, but the signi cance is stronger in the emerging markets economies. As the main objective of the paper, the study has found four of the selected countries to be bad news cases and eight of them are good news cases. / MT2016

技術分析包括圖表分析和技術指標分析。比較兩者，前者偏於主觀，並且解讀方式不一，而後者卻能用科學方法來考量。本研究論文先分析市場上流行的技術指標，移動平均線。交易員觀測兩條不同日數的移動平均線，從兩線相交處尋找進出市場的時機。從領域來看，兩條不同日數的移動平均線之差屬於一種帶通濾波器。本文將解釋帶通濾波器與市場進出規則之間的關係。除了移動平均線這種線性方法，我們同時考慮非線性的訊號處理工具。特別地，本研究採用近代提出的經驗模態分解法，得出類似移動平均線相交法的一種新交易策略。我們將文中提及的方法應用在香港及中國過去五年的股票市場，並給出數值結果以顯其效。 / Technical analysis includes chart pattern reading and stock market indicators. While the former is subjective and open to different interpretations, the latter is quantied in a more scientic way. The moving average, a popular market indicator, will be analyzed in this thesis. Traders monitor the crossovers of two moving averages with different durations to nd market entry timings. From the viewpoint of frequency domain, the difference of two such moving averages is found to be a band-pass filter. The relation between band-pass filter and market entry strategy is explained. Apartfrom linear methods such as the moving average,non linear signal processing tool is also studied. In particular,the modern empirical mode decomposition is applied to derive a new trading strategy similar to the moving average crossover rule. The introduced methods are put to the test in the Hong Kong and Chinese stock markets for the last five years. Numerical results are presented to show the performance of the methods. / Detailed summary in vernacular field only. / Lee, Tsz Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 64-66). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Linear Filters --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Frequency response --- p.13 / Chapter 2.3 --- Recursive filters --- p.16 / Chapter 2.4 --- Convolution theorem --- p.20 / Chapter 3 --- Momentum Indicators --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Momentum indicators --- p.24 / Chapter 3.3 --- Crossover of two moving averages --- p.25 / Chapter 3.4 --- MACD and acceleration indicators --- p.27 / Chapter 4 --- Profitability of Momentum Indicators --- p.33 / Chapter 4.1 --- Introduction --- p.33 / Chapter 4.2 --- Trading methodology --- p.34 / Chapter 4.3 --- Evaluating the performance --- p.36 / Chapter 4.4 --- Results of evaluation --- p.39 / Chapter 5 --- Empirical Mode Decomposition --- p.45 / Chapter 5.1 --- Introduction --- p.45 / Chapter 5.2 --- Instantaneous frequency --- p.46 / Chapter 5.3 --- Empirical mode decomposition --- p.47 / Chapter 5.4 --- Trading methodology --- p.50 / Chapter 5.5 --- Results of evaluation --- p.52 / Chapter 6 --- Discussions --- p.57 / Chapter A Descriptive Statistics and Additional Numerical Results --- p.60 / Bibliography --- p.64

published_or_final_version / abstract / Economics and Finance / Doctoral / Doctor of Philosophy

A research report presented in partial fulfilment (50%) of the requirements for the degree of Master of Commerce in Business Economics (Finance) in the School of Economic and Business Sciences at the University of Witwatersrand, Johannesburg, 6 October 2017 / This study tests the efficacy of the liquidity variables of Liu (2006) in determining the existence of a liquidity premium on the South African market and finds evidence of a significant liquidity effect. This factor is determined to be robust and to proxy for a different underlying effect than the Fama-French (1992) effects and the market risk premium. The analysis is performed through portfolio sorts and tests for difference of portfolio means, as well as both a univariate and multivariate regression analysis. The sample period covers 16 years from 2000 to 2015. The relationship between size and liquidity is clear, however liquidity is found to be separate from the size effect. This study recommends the use of a liquidity-augmented model for the analysis of asset returns in South Africa. / GR2018

Master of Commerce (Finance) in the Finance Division, School of Economic and Business Sciences at the University of the Witwatersrand, Johannesburg, 6 September 2017 / The aim of this study is to investigate the relationship between market liquidity changes on the Johannesburg Stock Exchange (JSE), and the market’s degree of efficiency. Market efficiency is characterised in terms of two philosophies: Fama’s (1970) Efficient Markets Hypothesis, and Shiller’s (1981; 2003) informational efficiency designation. Efficiency was tested using measures of return predictability, a random walk benchmark, and price volatility; liquidity was measured using market turnover. The tests were conducted on JSE Top 40 shares across three regimes, spanning January 2012 – June 2016. The regimes are demarcated by two structural breaks in the JSE’s microstructure: the 2012 trading platform upgrade, and the 2014 colocation centre launch. The results show that past order imbalances are a significant predictor of daily returns, although the significance of this predictability has dissipated over time. Return predictability is not influenced by liquidity. In fact, there is evidence that illiquidity weakens return predictability. Prices were closer to random walk benchmarks during the third regime. In consideration of informational efficiency, during the latter two regimes price volatility is greater during trading versus non-trading hours. This is coupled with an emergence of nonlinear return dependence, which is indicative of greater mispricing. Thus, over the three regimes, market efficiency improved in the sense of the EMH, but informational efficiency deteriorated. The study contributes to the field by: introducing an inverse measure of market efficiency; providing insight into the measure’s time variation and relation to liquidity; and demonstrating that market efficiency tests should incorporate its dual meanings, enabling richer understanding of their intersection. / GR2018

Lau Yuk Hoi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 109-110). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Notations --- p.vi / List of Propositions --- p.vii / List of Figures --- p.viii / List of Appendices --- p.x / Chapter Chapter 1. --- Introduction - The Reaction Approach --- p.1 / Chapter Chapter 2. --- Assumption for OLG Model --- p.7 / Chapter 2.1 --- Assumption A --- p.7 / Chapter Chapter 3. --- Equilibrium Conditions Without Fundamental Risk --- p.9 / Chapter 3.1 --- Price as a Weighted Average --- p.9 / Chapter 3.2 --- Determination of A and B --- p.11 / Chapter 3.2.1 --- Assumption B --- p.12 / Chapter 3.2.2 --- RE Line and NE Line --- p.13 / Chapter 3.2.3 --- Equilibrium values of A and B --- p.14 / Chapter 3.3 --- Rational Expectation on Price Variance (RV Line) --- p.16 / Chapter 3.4 --- Noisy Expectation on Price Variance (NV Line) --- p.18 / Chapter 3.4.1 --- DeLong's Model --- p.19 / Chapter 3.4.2 --- Bhushan's Model --- p.21 / Chapter 3.5 --- Change in Relative Perceived Variance --- p.23 / Chapter 3.5.1 --- General Problem of OLG Model in Noisy Trading --- p.23 / Chapter 3.5.2 --- Changes in Noise Traders' Beliefs --- p.24 / Chapter 3.5.3 --- "Relative Perceived Price Variance of n, θ" --- p.25 / Chapter 3.5.3.1 --- "Effect of Increasing θ on Price Variance, dC/dθ" --- p.26 / Chapter 3.5.3.2 --- "Effect of Increasing θ on Expected Price Level, dp/dθ" --- p.27 / Chapter Chapter 4. --- Equilibrium Conditions With Fundamental Risk --- p.31 / Chapter 4.1 --- Price as a Weighted Average --- p.32 / Chapter 4.2 --- Determination of A and B --- p.34 / Chapter 4.2.1 --- Assumption C --- p.34 / Chapter 4.2.2 --- RE Line and NE Line --- p.35 / Chapter 4.2.3 --- Equilibrium values of A and B --- p.36 / Chapter 4.3 --- Rational Expectation on return Variance (RV Line) --- p.37 / Chapter 4.4 --- Noisy Expectation on Return Variance (NV Line) --- p.40 / Chapter 4.4.1 --- De Long's Model --- p.41 / Chapter 4.4.2 --- Bhushan's Model --- p.42 / Chapter 4.5 --- Change in Relative Perceived Return Variance --- p.45 / Chapter 4.5.1 --- Specification of Noisy Expectation --- p.46 / Chapter 4.5.2 --- Relative Perceived Return Variance of n，Θ --- p.46 / Chapter 4.5.2.1 --- "Effect of Increasing Θ on Price Variance, dC/dΘ" --- p.47 / Chapter 4.5.2.2 --- "Effect of Increasing Θ on Expected Price Level, dp/dΘ" --- p.48 / Chapter 4.6 --- Relative Perceived Price Risk versus Relative Perceived Dividend Risk --- p.52 / Chapter Chapter 5. --- Conclusion and Discussion --- p.55 / Figures --- p.58 / Appendices --- p.86 / References --- p.109

Poon, Ka Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 62-63). / Abstracts in English and Chinese. / Abstract of the thesis entitled --- p.ii / 摘要 --- p.iii / Acknowledgements --- p.iv / Chapter Section 1. --- Introduction --- p.1 / Chapter Section 2. --- Review of Useful Concepts --- p.4 / Chapter 2.1 --- Terms and Methodologies - Pattern Recognition --- p.4 / Chapter 2.1.1 --- Rolling Windows --- p.4 / Chapter 2.1.2 --- Smoothing Function - Kernel Regression --- p.5 / Chapter 2.1.3 --- Filtering Function ´ؤ Search for Extrema --- p.6 / Chapter 2.1.4 --- Filtering Function - The Pattern Detection Algorithm --- p.7 / Chapter 2.1.5 --- Risk-adjustment Model --- p.10 / Chapter Section 3. --- Data and Methodology --- p.12 / Chapter 3.1 --- Data --- p.12 / Chapter 3.2 --- Methodology --- p.12 / Chapter Section 4. --- Results --- p.17 / Chapter Section 5. --- Further Extension --- p.21 / Chapter Section 6. --- Discussions and Conclusion --- p.22 / APPENDIX 1 --- p.23 / References --- p.62