This paper aims to investigate the apparent existence of two anomalies in the South African stock market based on regular strike action, namely the month of the year effect and seasonality across specific sectors of the Johannesburg Stock Exchange.

A research report submitted to the Faculty of Commerce, Law and Management at the University of the Witwatersrand in partial fulfilment of the requirements for the degree of MM in Finance and Investment March 2015 / Individual investors often do not have access to share trading information and even if they do, they may not be able to understand or accurately interpret this information. Investors rely on financial analysts’ forecasts and stock recommendations in order to make profitable investment decisions. The role of the financial analyst is an important one with two key objectives: earnings forecasts and stock recommendations (Loh and Mian 2006). These financial analysts play a significant role in the efficient functioning of global stock markets. The aim of the financial analyst is to evaluate shares trading on the stock market and their future price appreciation or depreciation to develop new buy, hold or sell recommendations to maximize shareholder wealth. The extant literature recognizes that new buy, hold and sell recommendations made by financial analysts have a substantial impact on the market (Womack, 1996). Research on financial analysts has become prevalent in financial literature with the promotion of financial analysts to the level of integral economic proxies worthy of individual examination (Bradshaw, 2011). The aim of this research report is to investigate whether financial analysts’ stock recommendations enhance or destruct shareholder wealth. The extant literature on financial analysts’ stock recommendations and forecasts suggests that the analysts’ recommendations have both a significant and an insignificant effect on stock prices in the market following the months after the change in recommendation is made. The accuracy of the financial analysts’ stock recommendations are measured in the months following the change in recommendation through determining if the recommendation outperforms the market benchmark. This report examines the effects of analysts’ recommendations on the performance of stocks on the Johannesburg Stock Exchange and concludes through determining if the share underperforms or outperforms the market benchmark surmising that to a varying degree there is value to be found in financial analysts’ stock recommendations for the individual investor.

A research report submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfilment (50%) of the requirements for degree of Master of Commerce in Finance. March 2016 / This study attempts to determine the possible effects of individualism on industry volatility. The implications of this for behavioural finance are extensive, showing firstly that different industries react differently to behavioural biases and secondly that overconfidence is a possible driver of the positive effect of individualism on industry volatility. The country selection process was relatively objective, taking two countries with high individualism indexes and two with low indexes and including one with a medium index value. The result was a sample of the United States of America, the United Kingdom, South Africa, China and Taiwan. The industry selection process was more subjective. Industries were selected which should have a higher propensity to behavioural biases with lower book to market ratios (software and computer services industry and pharmaceutical and biotechnology industry) and other industries which should not be as strongly affected by behavioural biases (banks, mining, oil and gas producers, and mobile telecommunications industries). In order to correct for ARCH effects the series’ were modelled using a GARCH (1, 1) model. The resulting residuals, which showed no autocorrelation, were then used to conduct panel data regressions on each of the industries. The results confirmed that individualism had a positive effect on volatility in the industries which were expected (software and computer services and pharmaceuticals and biotechnology industries). However, it was also determined that the banks industry was significantly affected by individualism, an effect which it was hypothesised, was due to the individualism of employees as opposed to investors. / MT2017

by Wai Chi-kin. / Abstract also in Chinese / Bibliography: leaves 174-175 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1982

Ng Anny. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 139-142). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Definition --- p.2 / Chapter 1.2 --- Thesis Organization --- p.3 / Chapter 2 --- Chinese Text Summarization Using Genetic Algorithm --- p.4 / Chapter 2.1 --- Introduction --- p.4 / Chapter 2.2 --- Related Work --- p.6 / Chapter 2.3 --- Genetic Algorithm Approach --- p.10 / Chapter 2.3.1 --- Fitness Function --- p.11 / Chapter 2.3.2 --- Genetic operators --- p.14 / Chapter 2.4 --- Implementation Details --- p.15 / Chapter 2.5 --- Experimental results --- p.19 / Chapter 2.6 --- Limitations and Future Work --- p.24 / Chapter 2.7 --- Conclusion --- p.26 / Chapter 3 --- Event Extraction from Chinese Financial News --- p.27 / Chapter 3.1 --- Introduction --- p.28 / Chapter 3.2 --- Method --- p.29 / Chapter 3.2.1 --- Data Set Preparation --- p.29 / Chapter 3.2.2 --- Positive Word --- p.30 / Chapter 3.2.3 --- Negative Word --- p.31 / Chapter 3.2.4 --- Window --- p.31 / Chapter 3.2.5 --- Event Extraction --- p.32 / Chapter 3.3 --- System Overview --- p.33 / Chapter 3.4 --- Implementation --- p.33 / Chapter 3.4.1 --- Event Type and Positive Word --- p.34 / Chapter 3.4.2 --- Company Name --- p.34 / Chapter 3.4.3 --- Negative Word --- p.36 / Chapter 3.4.4 --- Event Extraction --- p.37 / Chapter 3.5 --- Stock Database --- p.38 / Chapter 3.5.1 --- Stock Movements --- p.39 / Chapter 3.5.2 --- Implementation --- p.39 / Chapter 3.5.3 --- Stock Database Transformation --- p.39 / Chapter 3.6 --- Performance Evaluation --- p.40 / Chapter 3.6.1 --- Performance measures --- p.40 / Chapter 3.6.2 --- Evaluation --- p.41 / Chapter 3.7 --- Conclusion --- p.45 / Chapter 4 --- Mining Frequent Episodes --- p.46 / Chapter 4.1 --- Introduction --- p.46 / Chapter 4.1.1 --- Definitions --- p.48 / Chapter 4.2 --- Related Work --- p.50 / Chapter 4.3 --- Double-Part Event Tree for the database --- p.56 / Chapter 4.3.1 --- Complexity of tree construction --- p.62 / Chapter 4.4 --- Mining Frequent Episodes with the DE-tree --- p.63 / Chapter 4.4.1 --- Conditional Event Trees --- p.66 / Chapter 4.4.2 --- Single Path Conditional Event Tree --- p.67 / Chapter 4.4.3 --- Complexity of Mining Frequent Episodes with DE-Tree --- p.67 / Chapter 4.4.4 --- An Example --- p.68 / Chapter 4.4.5 --- Completeness of finding frequent episodes --- p.71 / Chapter 4.5 --- Implementation of DE-Tree --- p.71 / Chapter 4.6 --- Method 2: Node-List Event Tree --- p.76 / Chapter 4.6.1 --- Tree construction --- p.79 / Chapter 4.6.2 --- Order of Position Bits --- p.83 / Chapter 4.7 --- Implementation of NE-tree construction --- p.84 / Chapter 4.7.1 --- Complexity of NE-Tree Construction --- p.86 / Chapter 4.8 --- Mining Frequent Episodes with NE-tree --- p.87 / Chapter 4.8.1 --- Conditional NE-Tree --- p.87 / Chapter 4.8.2 --- Single Path Conditional NE-Tree --- p.88 / Chapter 4.8.3 --- Complexity of Mining Frequent Episodes with NE-Tree --- p.89 / Chapter 4.8.4 --- An Example --- p.89 / Chapter 4.9 --- Performance evaluation --- p.91 / Chapter 4.9.1 --- Synthetic data --- p.91 / Chapter 4.9.2 --- Real data --- p.99 / Chapter 4.10 --- Conclusion --- p.103 / Chapter 5 --- Mining N-most Interesting Episodes --- p.104 / Chapter 5.1 --- Introduction --- p.105 / Chapter 5.2 --- Method --- p.106 / Chapter 5.2.1 --- Threshold Improvement --- p.108 / Chapter 5.2.2 --- Pseudocode --- p.112 / Chapter 5.3 --- Experimental Results --- p.112 / Chapter 5.3.1 --- Synthetic Data --- p.113 / Chapter 5.3.2 --- Real Data --- p.119 / Chapter 5.4 --- Conclusion --- p.121 / Chapter 6 --- Mining Frequent Episodes with Event Constraints --- p.122 / Chapter 6.1 --- Introduction --- p.122 / Chapter 6.2 --- Method --- p.123 / Chapter 6.3 --- Experimental Results --- p.125 / Chapter 6.3.1 --- Synthetic Data --- p.126 / Chapter 6.3.2 --- Real Data --- p.129 / Chapter 6.4 --- Conclusion --- p.131 / Chapter 7 --- Conclusion --- p.133 / Chapter A --- Test Cases --- p.135 / Chapter A.1 --- Text 1 --- p.135 / Chapter A.2 --- Text 2 --- p.137 / Bibliography --- p.139

This dissertation aims to investigate the asset pricing implications of the stock option's implied volatility term structure. We mainly focus on two directions: the volatility term structure of the market and the volatility term structure of individual stocks. The market volatility term structure, which is calculated from prices of index options with different expirations, reflects the market's expectation of future volatility of different horizons. So the market volatility term structure incorporates information that is not captured by the market volatility itself. In particular, the slope of the volatility term structure captures the expected volatility trend. In the first part of the thesis, we investigate whether the market volatility term structure slope is a priced source of risk or not. We find that stocks with high sensitivities to the proxies of the VIX term structure slope exhibit high returns on average. We further estimate the premium for bearing the VIX slope risk to be approximately 2.5% annually and statistically significant. The effect cannot be explained by other common risk factors, such as the market excess return, size, book-to-market, momentum, liquidity and market volatility. We extensively investigate the robustness of our empirical results and find that the effect of the VIX term structure risk is robust. Within the context of ICAPM, the positive price of VIX term structure risk indicates that it is a state variable which positively affects the future investment opportunity set. In the second part of the thesis, we provide a stylized model that explains our empirical results. We build a regime-switching rare disaster model that allows disasters to have short and long durations. Our model indicates that a downward sloping VIX term structure corresponds to a potential long disaster and an upward sloping VIX term structure corresponds to a potential short disaster. It further implies that stocks with high sensitivities to the VIX slope have high loadings on the disaster duration risk, thus earn higher risk premium. These implications are consistent with our empirical results. In the last part, we study the relationship between individual stock's volatility term structure and the stock's future return. We use a measure of stock's implied volatility term structure slope, defined as the difference between 3-month and 1-month implied volatility from at-the-money options, to demonstrate that option prices contain important information for the underlying equities. We show that option volatility term structure slopes are significant in explaining future equity returns in the cross-section. And we further find evidence that the implied volatility term structure is a measure of event risk: firms with the most negative volatility term structure are those for which the market anticipates news that may affect stock price within one month. Relevant events include, but are not limited to, earnings announcements.

Tang Him John. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 76-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.1.1 --- Linear Time Series --- p.2 / Chapter 1.1.2 --- Mixture Models --- p.3 / Chapter 1.1.3 --- EM algorithm --- p.6 / Chapter 1.1.4 --- Model Selection --- p.6 / Chapter 1.2 --- Main Objectives --- p.7 / Chapter 1.3 --- Outline of this thesis --- p.7 / Chapter 2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- "AR, MA, and ARMA" --- p.10 / Chapter 2.1.2 --- Stationarity --- p.11 / Chapter 2.1.3 --- ARCH and GARCH --- p.12 / Chapter 2.1.4 --- Gaussian mixture --- p.13 / Chapter 2.1.5 --- EM and GEM algorithms --- p.14 / Chapter 2.2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.16 / Chapter 2.3 --- Estimation of Gaussian mixture ARMA-GARCH model --- p.17 / Chapter 2.3.1 --- Autocorrelation and Stationarity --- p.20 / Chapter 2.3.2 --- Model Selection --- p.24 / Chapter 2.4 --- Experiments: First Step Prediction --- p.26 / Chapter 2.5 --- Chapter Summary --- p.28 / Chapter 2.6 --- Notations and Terminologies --- p.30 / Chapter 2.6.1 --- White Noise Time Series --- p.30 / Chapter 2.6.2 --- Lag Operator --- p.30 / Chapter 2.6.3 --- Covariance Stationarity --- p.31 / Chapter 2.6.4 --- Wold's Theorem --- p.31 / Chapter 2.6.5 --- Multivariate Gaussian Density function --- p.32 / Chapter 3 --- Finite Mixture-of-Expert ARMA-GARCH Model --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.1.1 --- Mixture-of-Expert --- p.34 / Chapter 3.1.2 --- Alternative Mixture-of-Expert --- p.35 / Chapter 3.2 --- ARMA-GARCH Finite Mixture-of-Expert Model --- p.36 / Chapter 3.3 --- Estimation of Mixture-of-Expert ARMA-GARCH Model --- p.37 / Chapter 3.3.1 --- Model Selection --- p.38 / Chapter 3.4 --- Experiments: First Step Prediction --- p.41 / Chapter 3.5 --- Second Step and Third Step Prediction --- p.44 / Chapter 3.5.1 --- Calculating Second Step Prediction --- p.44 / Chapter 3.5.2 --- Calculating Third Step Prediction --- p.45 / Chapter 3.5.3 --- Experiments: Second Step and Third Step Prediction . --- p.46 / Chapter 3.6 --- Comparison with Other Models --- p.50 / Chapter 3.7 --- Chapter Summary --- p.57 / Chapter 4 --- Stable Estimation Algorithms --- p.58 / Chapter 4.1 --- Stable AR(1) estimation algorithm --- p.59 / Chapter 4.2 --- Stable AR(2) Estimation Algorithm --- p.60 / Chapter 4.2.1 --- Real p1 and p2 --- p.61 / Chapter 4.2.2 --- Complex p1 and p2 --- p.61 / Chapter 4.2.3 --- Experiments for AR(2) --- p.63 / Chapter 4.3 --- Experiment with Real Data --- p.64 / Chapter 4.4 --- Chapter Summary --- p.65 / Chapter 5 --- Conclusion --- p.66 / Chapter 5.1 --- Further Research --- p.69 / Chapter A --- Equation Derivation --- p.70 / Chapter A.1 --- First Derivatives for Gaussian Mixture ARMA-GARCH Esti- mation --- p.70 / Chapter A.2 --- First Derivatives for Mixture-of-Expert ARMA-GARCH Esti- mation --- p.71 / Chapter A.3 --- First Derivatives for BYY Harmony Function --- p.72 / Chapter A.4 --- First Derivatives for stable estimation algorithms --- p.73 / Chapter A.4.1 --- AR(1) --- p.74 / Chapter A.4.2 --- AR(2) --- p.74 / Bibliography --- p.80

Leung Lok-yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 70-71). / Abstracts in English and Chinese. / Chapter 1. --- Introduction and Literature Review --- p.1 / Chapter 2. --- Methodology --- p.4 / Chapter A. --- Momentum Strategies / Chapter B. --- Relative Strength Indexes / Chapter 3. --- Data --- p.13 / Chapter 4. --- Emirical Findings --- p.15 / Chapter A. --- Momentum Strategies / Chapter B. --- Relative Strength Indexes / Chapter 5. --- Conclusion --- p.37 / Chapter 6. --- Tables --- p.39 / Chapter 7. --- Bibliograhy --- p.70

Fung Pui Cheong Gabriel. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 88-93). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Abstract (Chinese) --- p.ii / Acknowledgement --- p.iii / Contents --- p.v / List of Figures --- p.ix / List of Tables --- p.x / Chapter Part I --- The Very Beginning --- p.1 / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Contributions --- p.3 / Chapter 1.2 --- Dissertation Organization --- p.4 / Chapter 2 --- Problem Formulation --- p.6 / Chapter 2.1 --- Defining the Prediction Task --- p.6 / Chapter 2.2 --- Overview of the System Architecture --- p.8 / Chapter Part II --- Literatures Review --- p.11 / Chapter 3 --- The Social Dynamics of Financial Markets --- p.12 / Chapter 3.1 --- The Collective Behavior of Groups --- p.13 / Chapter 3.2 --- Prediction Based on Publicity Information --- p.16 / Chapter 4 --- Time Series Representation --- p.20 / Chapter 4.1 --- Technical Analysis --- p.20 / Chapter 4.2 --- Piecewise Linear Approximation --- p.23 / Chapter 5 --- Text Classification --- p.27 / Chapter 5.1 --- Document Representation --- p.28 / Chapter 5.2 --- Document Pre-processing --- p.30 / Chapter 5.3 --- Classifier Construction --- p.31 / Chapter 5.3.1 --- Naive Bayes (NB) --- p.31 / Chapter 5.3.2 --- Support Vectors Machine (SVM) --- p.33 / Chapter Part III --- Mining Financial Time Series and Textual Doc- uments Concurrently --- p.36 / Chapter 6 --- Time Series Representation --- p.37 / Chapter 6.1 --- Discovering Trends on the Time Series --- p.37 / Chapter 6.2 --- t-test Based Split and Merge Segmentation Algorithm ´ؤ Splitting Phrase --- p.39 / Chapter 6.3 --- t-test Based Split and Merge Segmentation Algorithm - Merging Phrase --- p.41 / Chapter 7 --- Article Alignment and Pre-processing --- p.43 / Chapter 7.1 --- Aligning News Articles to the Stock Trends --- p.44 / Chapter 7.2 --- Selecting Positive Training Examples --- p.46 / Chapter 7.3 --- Selecting Negative Training Examples --- p.48 / Chapter 8 --- System Learning --- p.52 / Chapter 8.1 --- Similarity Based Classification Approach --- p.53 / Chapter 8.2 --- Category Sketch Generation --- p.55 / Chapter 8.2.1 --- Within-Category Coefficient --- p.55 / Chapter 8.2.2 --- Cross-Category Coefficient --- p.56 / Chapter 8.2.3 --- Average-Importance Coefficient --- p.57 / Chapter 8.3 --- Document Sketch Generation --- p.58 / Chapter 9 --- System Operation --- p.60 / Chapter 9.1 --- System Operation --- p.60 / Chapter Part IV --- Results and Discussions --- p.62 / Chapter 10 --- Evaluations --- p.63 / Chapter 10.1 --- Time Series Evaluations --- p.64 / Chapter 10.2 --- Classifier Evaluations --- p.64 / Chapter 10.2.1 --- Batch Classification Evaluation --- p.69 / Chapter 10.2.2 --- Online Classification Evaluation --- p.71 / Chapter 10.2.3 --- Components Analysis --- p.74 / Chapter 10.2.4 --- Document Sketch Analysis --- p.75 / Chapter 10.3 --- Prediction Evaluations --- p.75 / Chapter 10.3.1 --- Simulation Results --- p.77 / Chapter 10.3.2 --- Hit Rate Analysis --- p.78 / Chapter Part V --- The Final Words --- p.80 / Chapter 11 --- Conclusion and Future Work --- p.81 / Appendix --- p.84 / Chapter A --- Hong Kong Stocks Categorization Powered by Reuters --- p.84 / Chapter B --- Morgan Stanley Capital International (MSCI) Classification --- p.85 / Chapter C --- "Precision, Recall and F1 measure" --- p.86 / Bibliography --- p.88

A partir da década de 90, a abertura e liberalização de fluxos de capitais, somados à intensificação dos processos de fusões, aquisições e privatizações evidenciaram a necessidade de se saber qual é o valor de uma empresa e quais variáveis o afetam. Ao encontro a esta necessidade, este trabalho foi desenvolvido para investigar as variáveis consideradas como direcionadores de valor e, em específico macrodirecionadores de valor, e sua relação com o preço da ação. As implicações de se saber se um macrodirecionador de valor pode predizer e provocar alterações no preço da ação, são de ampla utilidade e importância tanto para investidores quanto para os gestores da empresa. Este trabalho objetivou saber se o desempenho dos macrodirecionadores de valor pode ser, e de que forma, preditor do desempenho do preço das ações das empresas de capital aberto mais líquidas da Bolsa de Valores de São Paulo, durante o período de 1994 a 2005. Além disso, buscou entender também, se uma variação em um macrodirecionador pode provocar e explicar uma variação no preço da ação. Para a realização da pesquisa utilizou como modelo estatístico a Causalidade de Granger e a Auto-Regressão Vetorial. Os resultados foram poucos significativos ou revelaram relações pouco consistentes para a maioria das empresas da amostra. Contudo, os resultados também revelaram a possibilidade de determinados macrodirecionadores de valor poderem ser preditores do preço da ação, para um número restrito de empresas da amostra. Esta possibilidade de relação foi apontada para os macrodirecionadores de valor: taxa de crescimento em vendas na Embraer e Usiminas; margem de lucro operacional na Embraer, Eletrobrás e Companhia Siderúrgica Nacional; e taxa de investimento adicional na Eletrobrás. / Since the decade of 90, liberalization of capitals markets, added to the processes of merger and acquisitions had evidenced the need to know which is the right value of a company and which drives it. This paper was developed to investigate the considered value drivers of a stock. This paper intended to understand the relation between value drivers and stock prices. The benefits of this knowledge may help managers and investor in they decisions. The paper focus was investigated if a change in value drivers can predict and motive a stock price change. This work objective knowing if the performance of value drivers can be predictor of stock price performance, to Brazilians enterprises, between 94 to 2005. Moreover, it searched to also understand, if a variation in a value drives can motive and explain a variation in the stock price. To accomplishment the research it used two statistical model: Granger Cause and Auto-Regression Vector (VAR). The results had few significant ones or had little disclosed consistent relations for the majority of the sample companies. However, the results had also disclosed the possibility of definitive value drivers being able to predict and motive stock prices. This possibility relation was pointed for some value drivers: revenue growth in Embraer and Usiminas; earnings before interest and tax in the Embraer, Eletrobrás and Companhia Siderúrgica Nacional; and tax of additional investment in the Eletrobrás.