M.B.A / The aim of this dissertation is to determine if there is any evidence that supports a "50" and a "200" day moving average share trading strategy to select, buy and sell shares quoted on the Johannesburg Securities Exchange (JSE) Main Board, in order to determine if a "50" and a "200" day moving average share trading strategy will be appropriate to use, in order to make share trading profits in excess of the return generated by the JSE Overall Index. 1.4 0 .ACTIFVES o To evaluate fundamental analysis in respect of the quality of information (mainly at a company level) available to investors as the basis on which decisions to buy and sell shares are made. o To evaluate previous research undertaken in technical analysis with respect to the use and application of moving averages as a trading strategy in making share selections as well as buy and sell decisions. 14 Analyse historic price data on individual, randomly selected shares from the total population of all main board (1.6.5) listed shares quoted on the Johannesburg

This study investigates the independence assumption of the theory of random walks in stock market prices through the simulation of the moving average strategy. In the process of doing so, three related questions are examined: (1) Does the past relative volatility of a stock furnish a useful indication of its future behavior? (2) Is the performance of the decision rule improved by applying it to those securities which are likely to be highly volatile? (3) Does positive dependence in successive monthly price changes exist? The purpose of Test No. 1 was to gauge the tendency for a stock's relative volatility to remain constant over two adjacent intervals of time. As measured by the coefficient of variation, the volatility of each of the 200 securities was computed over the 1936 to 1945 and 1946 to 1955 decades. In order to evaluate the strength of the relationship between these paired observations, a rank correlation analysis was performed. The results indicated a substantial difference in relative volatility for each security over the two ten-year periods. In Test No. 2 a different experimental design was employed to determine whether the relative volatility of a stock tended to remain within a definite range over time. According to their volatility in the 1936 to 1945 period, the 200 securities were divided into ten groups. Portfolio No. 1 contained the twenty most volatile securities while Portfolio No. 2 consisted of the next twenty most volatile, etc. An average coefficient of variation was calculated for each group over the periods, 1936 to 1945 and 1946 to 1955. The rank correlation analysis on these ten paired observations revealed that the most volatile securities, as a group, tended to remain the most volatile. Test No. 3 consisted of the application of the moving average strategy (for long positions only) to forty series of month-end prices covering the interval, 1956 to 1966. These securities had demonstrated a high relative volatility over the previous decade and, on the basis of the findings reported in Test No. 2, it was forecasted that they would be the most volatile of the sample of 200 in the period under investigation. Four different moving averages ranging from three to six months, and thirteen different thresholds ranging from 2 to 50 per cent were simulated. The results of the simulation showed the moving average strategy to be much inferior to the two buy-and-hold models. Every threshold regardless of the length of the moving average yielded a negative return. In addition, the losses per threshold were spread throughout the majority of stocks. Altogether, therefore, considerable evidence was found in favour of the random walk theory of stock price behavior. / Business, Sauder School of / Graduate

Technology has evolved the last 20 years, making both the stock market and media operate in real time. The advancement of technology has increased trading activity and the number of investors who enter the stock market. Alongside, media has expanded itself into the internet and can, due to the advancement, provide information faster and in higher volumes through their channels. Being that, a limited number of follow-ups have been made regarding the impact of media on stock price and no studies have been made to investigate how stock price correlates with negative media. This generated the aim of this study to investigate and analyze the impact of “Bad Media Attention” on stock price. The thesis was conducted as an exploratory research study that collected secondary data from Avanza. Furthermore, the methodology in this study was structured and performed to best answer the research question (RQ1): Does “Bad Media Attention” have an impact on stock price?” The results showed that the majority of news defined as ”Bad Media Attention” had no statistically significant impact on stock price. Also, the study found no consistent statistically significant correlation between “Bad Media Attention” and stock price. However, the small number of significant variables tends to have a negative correlation between “Bad Media Attention” and stock price. Therefore, this research contributed to the stock market field in a number of ways. First, by showing a majority of news events, defined as “Bad Media Attention”, had no statistically significant impact on stock price for large cap companies on the OMX Stockholm 30 Stock Exchange. Second, the results and analysis may help to better grasp the impact of “Bad Media Attention”. Third, this study provided more insight in the research area and raised awareness of this particular phenomenon, and will for this reason be a valuable discovery for future research.

published_or_final_version / Business Administration / Master / Master of Business Administration

by Lau Chung-Hing. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1988. / Includes bibliographical references.

by Ip Ka Tsun Anthony and Tang Ying Wa. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 42-46). / ABSTRACT --- p.i / TABLE OF CONTENTS --- p.iii / LIST OF TABLES --- p.iv / ACKNOWLEDGMENTS --- p.v / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II . --- LITERATURE REVIEW --- p.4 / Selection Criteria of the Neglected Markets --- p.4 / Market Review --- p.4 / Day-of-the-Week Effect --- p.9 / Month- of - the - Year Effect --- p.11 / Spill´ؤOver Effect Across National Stock Markets --- p.11 / Granger Causality Between Aggregate Stock Price and Trading Volume --- p.13 / Chapter III. --- DATA and METHODOLOGY --- p.16 / Day-of-the-Week Effect and Month-of-the-Year Effect --- p.16 / Spill-Over Effect Across National Stock Markets and Granger Causality Between Aggregate Stock Price and Trading Volume --- p.18 / Chapter IV. --- EMPIRICAL RESULTS --- p.24 / Day-of-the-Week Effect --- p.24 / Month-of-the-Year Effect --- p.26 / Spill-Over Effect Across National Stock Markets --- p.28 / Granger Causality Between Aggregate Stock Price and Trading Volume --- p.31 / Chapter V. --- CONCLUSION --- p.36 / Direction of Further Studies --- p.38 / APPENDIX --- p.40 / BIBLIOGRAPHY --- p.42

by Leung Kwok Chu, Wong Cheuk Fung. / Thesis (M.B.A.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 54-55). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iv / ACKNOWLEDGMENTS --- p.vi / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Technical Analysis --- p.2 / The Characteristics and Efficiency of China's Equity Markets --- p.3 / Market Participants --- p.4 / Transaction Costs and Tradability of Shares --- p.5 / Availability of Information --- p.7 / Implication on Weak Form Market Efficiency --- p.8 / Relative Strength Index --- p.10 / Chapter II. --- LITERATURE REVIEW --- p.12 / Chapter III. --- METHODOLOGY --- p.15 / Primary Research --- p.15 / Source of Data --- p.15 / Spreadsheet Calculation Procedure --- p.16 / Hypothesis Testing --- p.18 / The First Type of Tests --- p.18 / The Second Type of Tests --- p.19 / The Third Type of Tests --- p.20 / Chapter IV. --- RESEARCH FINDINGS --- p.21 / Abnormal Returns Obtained by Following RSI Trading Rules --- p.21 / A-shares --- p.21 / Buy signals --- p.21 / Interpretations of buy signals in A-share markets --- p.22 / Sell signals --- p.22 / Interpretations of sell signals in A-share markets --- p.23 / B-shares --- p.25 / Buy signals --- p.25 / Interpretations of buy signals in B-share markets --- p.25 / Sell signals --- p.26 / Interpretations of sell signals in B-share markets --- p.27 / Chapter V. --- ADDITIONAL RESEARCHES ON B-SHARE MARKETS --- p.30 / Findings on Additional Researches on B-share Markets --- p.30 / Interpretations of Findings on Additional Researches on B-share Markets --- p.31 / Chapter VI. --- ADDITIONAL RESEARCHES ON A-SHARE MARKETS --- p.32 / Correlation between Abnormal Return and Volume Turnover --- p.33 / Findings on Correlation between Abnormal Return and Volume Turnover --- p.33 / Interpretations of Findings on Correlation between Abnormal Return and Volume Turnover --- p.33 / Correlation between Abnormal Return and Market Value --- p.34 / Findings on Correlation between Abnormal Return and Market Value --- p.34 / Interpretations of Findings on Correlation between Abnormal Return and Market Value --- p.35 / Chapter VII. --- CONCLUSIONS --- p.37 / Chapter VIII. --- LIMITATIONS --- p.39 / Chapter IX. --- FURTHER STUDIES RECOMMENDED --- p.42 / APPENDIX --- p.44 / BIBLIOGRAPHY --- p.54

published_or_final_version / abstract / toc / Economics and Finance / Master / Master of Economics

published_or_final_version / Statistics / Master / Master of Philosophy

Forecasting volatility with precision in financial market is very important. This paper examines the use of various forms of GARCH models for forecasting volatility. Three financial data sets from Japan (NIKKEI 225 index), the United States (Standard & Poor 500) and Germany (DAX index) are considered. A number of GARCH models, such as EGARCH, IGARCH, TGARCH, PGARCH and QGARCH models with normal distribution and student’s t distribution are used to fit the data sets and to forecast volatility. The Maximum Likelihood method and the Bayesian approach are used to estimate the parameters in the family of the GARCH models. The results show that the QGARCH model under student’s t distribution is the precise model for the NIKKEI 225 index in terms of fitting the data and forecasting volatility. The TGARCH under the student’s t distribution fits the S&P 500 index data better while the traditional GARCH model under the same distribution performs better in forecasting volatility. The PGARCH with student’s t distribution is the precise model for the DAX index in terms of fitting the data and forecasting volatility. / Department of Mathematical Sciences