Thesis (M.Soc.Sc.)--University of Hong Kong. 1989. / Also available in print.

M.Comm. / The availability of large amounts of information and increases in computing power have facilitated the use of more sophisticated and effective technologies to analyse financial markets. The use of neural networks for financial time series forecasting has recently received increased attention. Neural networks are good at pattern recognition, generalisation and trend prediction. They can learn to predict next week's Dow Jones or flaws in concrete. Traditional methods used to analyse financial markets include technical and fundamental analysis. These methods have inherent shortcomings, which include bad timing of trading signals generated, and non-continuous data on which analysis is based. The purpose of the study was to create a tool with which to forecast financial time series on the Johannesburg Stock Exchange (JSE). The forecasted time series information was used to generate trading signals. A study of the building blocks of neural networks was done before the neural network was designed. The design of the neural network included data choice, data collection, calculations, data pre-processing and the determination of neural network parameters. The neural network was trained and tested with information from the financial sector of the JSE. The neural network was trained to predict share prices 4 days in advance with a Multiple Layer Feedforward Network (MLFN). The mean square error on the test set was 0.000930, with all test data values scaled between 0.1 - 0.9 and a sample size of 160. The prediction results were tested with a trading system, which generated a trade yielding 20 % return in 22 days. The neural network generated excellent results by predicting prices in advance. This enables better timing of trades and efficient use of capital. However, it was found that the price movement on the test set within the 4-day prediction period seldom exceeded the cost of trades, resulting in only one trade over a 5-month period for one security. This should not be a problem if all securities on the JSE are analysed for profitable trades. An additional neural network could also be designed to predict price movements further ahead, say 8 days, to assist the 4-day prediction

Please read the abstract in the section 00front of this document / Dissertation (MSc (Mathematics of Finance))--University of Pretoria, 2006. / Mathematics and Applied Mathematics / unrestricted

This thesis presents three models of asset pricing involving non-competitive behavior and asymmetric information. In the first model, a risk averse investor with private information about dividends trades shares over an infinite time horizon with risk neutral uninformed agents. The informed investor trades strategically in equilibrium. The second model also involves an infinite time horizon, but all agents are risk averse and equally informed about dividends. Non-competitive behavior is exogenously specified; price takers trade shares with a strategic investor who accounts for the effects of her trades on the stock price. In this case, an endogenous information asymmetry arises in equilibrium. Closed form equilibria are derived for both models and implications for price dynamics are explored. While the first model constitutes a new extension of the multiperiod Kyle model of insider trading, the second model generates more interesting price dynamics. If the strategic investor manages a large mutual fund, significant risk premia and price volatility may arise in equilibrium. In fact, if mutual fund participation is sufficiently widespread, multiple equilibria may exist. The third model extends the multiperiod Kyle model to a case where the insider observes a noisy signal of the stock's terminal liquidation value. An equilibrium much like Kyle's is derived. Price tends toward value over time, and stock price volatility depends on both the drift and volatility of the insider's private signal. Like the Kyle model, the insider's trading activity leaves no detectable trace in trading volume, expected returns, or price volatility. / Business, Sauder School of / Finance, Division of / Graduate

Submitted in accordance with the requirements for the degree of Master of Management in the subject Finance and investments at the University of Witwatersrand 2017 / Although many studies have been done to determine the relationship between financial development and cost of equity capital in various markets, few have focused on the African emerging and frontier markets. This research therefore investigates the relationship between financial development and cost of equity capital in the African Emerging and Frontier Markets. Stock market development and banking sector development are both used as proxies for financial development in this study whilst cost of equity is determined using CAPM. The study is based on five emerging and frontier markets (Egypt, Kenya, Morocco, Nigeria and South Africa). The research finds that both measures of stock market development (stock market capitalisation to GDP ratio and stock market liquidity/turnover to GDP ratio) tend to reduce cost of equity in the African emerging and frontier markets. In a similar fashion, the banking sector development was also found to be negatively related to cost of equity / MT 2018

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Kong Tze-shan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 60-62). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.5 / Chapter 2.1 --- Moving Average --- p.5 / Chapter 2.2 --- Other Trading Rules --- p.9 / Chapter 2.3 --- Share Repurchase --- p.12 / Chapter 2.3.1 --- Types of Share Repurchase --- p.12 / Chapter 2.3.2 --- Previous Studies on Relationship between Share Repurchase and Stock Price --- p.14 / Chapter 3 --- Regulations and Facts of Share Repurchase in Hong Kong --- p.19 / Chapter 4 --- Data Summary --- p.23 / Chapter 4.1 --- Description on Hong Kong Stock Market --- p.23 / Chapter 4.2 --- Description on Hang Seng Index --- p.24 / Chapter 4.3 --- Description on Stock Price Series --- p.25 / Chapter 4.4 --- Description on Repurchase Data --- p.26 / Chapter 5 --- Profitability of Technical Trading Rule --- p.30 / Chapter 5.1 --- Moving Average --- p.30 / Chapter 5.2 --- Result of Individual Stocks --- p.32 / Chapter 5.3 --- Overall Result for 25 Stocks Tested --- p.35 / Chapter 5.4 --- Using short moving averages rather than current stock price --- p.37 / Chapter 6 --- Profitability with transaction cost --- p.39 / Chapter 6.1 --- Result of Individual Stock --- p.39 / Chapter 6.2 --- Sharpe Ratio of 25 Stocks Tested --- p.40 / Chapter 7 --- Profitability with Share Repurchase Dates Removed --- p.42 / Chapter 7.1 --- Removing Share Repurchase Dates --- p.42 / Chapter 7.2 --- Result of Individual Stock --- p.43 / Chapter 7.3 --- Overall Results for 10 Stocks Tested --- p.44 / Chapter 7.4 --- Removing Repurchase Dates of 28 Non-HSI Constituent Stocks --- p.47 / Chapter 8 --- Further discussion --- p.51 / Chapter 8.1 --- Basic differences in market structure --- p.51 / Chapter 8.2 --- Difference between central bank intervention and share repurchase --- p.52 / Chapter 8.2.1 --- Motivation of central bank intervention --- p.53 / Chapter 8.2.2 --- Motivation of share repurchase --- p.53 / Chapter 9 --- Conclusion --- p.57

Hung, Chun Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 71-74). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgement --- p.iii / Table of Content --- p.iv / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Hong Kong securities market background --- p.2 / Chapter 1.2 --- Purpose and brief results --- p.4 / Chapter 1.3 --- Organization of the paper --- p.5 / Chapter 2. --- Literature Review --- p.6 / Chapter 2.1 --- Theoretical Studies --- p.6 / Chapter 2.2 --- Empirical Studies --- p.8 / Chapter 3. --- Methodology --- p.14 / Chapter 3.1 --- Hypothesis development --- p.14 / Chapter 3.2 --- Data and Sample Characteristics --- p.16 / Chapter 3.3 --- Sample selection rules --- p.17 / Chapter 3.4 --- Variables definitions --- p.19 / Chapter 3.5 --- Estimation of market betas (pre-ranking and post-ranking) --- p.23 / Chapter 3.5.1 --- Betas estimation procedure --- p.23 / Chapter 3.5.2 --- Results and findings --- p.25 / Chapter 4. --- Size- Dispersion Portfolio Strategy --- p.27 / Chapter 4.1 --- Formation of size-beta portfolio --- p.27 / Chapter 4.2 --- Results and findings --- p.28 / Chapter 5. --- Fama-MacBeth cross-sectional regressions --- p.32 / Chapter 5.1 --- Relation between dispersion and other firm characteristics --- p.32 / Chapter 5.2 --- Relation between future stocks returns and firm characteristics --- p.33 / Chapter 5.3 --- Robustness check --- p.38 / Chapter 5.3.1 --- Sub-period regressions --- p.38 / Chapter 5.4 --- Possible Explanations --- p.39 / Chapter 6. --- Conclusion Remarks --- p.44 / Chapter 6.1 --- Conclusion --- p.44 / Chapter 6.2 --- Limitations and future direction --- p.45 / Tables --- p.47 / Table 1 Key statistics for the Hong Kong stock market --- p.47 / "Table 2 Sectoral distribution of market capitalization (per cent of total),1997-2006" --- p.48 / "Table 3 Market capitalization: top twenty firms (percentage of total market), 2006" --- p.49 / Table 4 Summary of empirical literature of dispersion on stock returns --- p.50 / Table 5 Summary Statistics for 70 sample stocks: January 1997 to December 2003 --- p.51 / Table 5 Summary Statistics for 70 sample stocks: January 1997 to December 2003(continue) --- p.52 / Table 5 Summary Statistics for 70 sample stocks: January 1997 to December 2003(continue) --- p.53 / Table 6 Sample properties based on sectoral distribution --- p.54 / Table 7 Descriptive statistics for the analysts´ة forecasts dispersion: 1997-2003 --- p.55 / Table 8 Properties of the nine size-beta portfolio for the sample period from January 1997 to December 2003 --- p.56 / Table 9 Mean and Median Portfolio Returns by Size and Dispersion in Analysts´ة Forecasts --- p.57 / Table 9 Mean and Median Portfolio Returns by Size and Dispersion in Analysts´ة Forecasts --- p.58 / Table 10 Mean Portfolio Dispersion by Size and Dispersion in Analysts´ة Forecasts --- p.59 / Table 11 Fama-MacBeth cross-sectional regressions of analysts´ة forecasts dispersion on lagged firm characteristics --- p.60 / Table 12 Fama-MacBeth cross-sectional regressions of Stock excess returns on lagged firm characteristics --- p.61 / Table 12 Fama-MacBeth cross-sectional regressions of Stock excess returns on lagged firm characteristics (continue) --- p.62 / Table 13 Overall monthly correlation matrix between explanatory variables for the period January 1997 to December 2003 --- p.63 / Table 15 Fama-MacBeth cross-sectional regressions of Stock excess returns on lagged firm characteristics (second sub-period) --- p.66 / Table 15 Fama-MacBeth cross-sectional regressions of Stock excess returns on lagged firm characteristics (second sub-period) (continue) --- p.67 / Figures --- p.68 / Figure 1 Growth trend of the Hong Kong stock market --- p.68 / Figure 2 Equities funds raised by H shares enterprise for GEM --- p.69 / Appendix one --- p.70 / References --- p.71

by Lam King Wan. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 72-73). / ABSTRACT --- p.ii. / TABLE OF CONTENTS --- p.iv. / Section / Chapter I. --- OBJECTIVE --- p.1 / Chapter II. --- INTRODUCTION --- p.3 / The principal investment risk --- p.3 / Effect of risk on investment --- p.4 / Investors' concern for investment risk --- p.6 / Chapter III. --- THE INPUT PARAMETERS --- p.9 / Chapter IV. --- LITERATURE REVIEW --- p.15 / What is an artificial neural network? --- p.15 / What is a neuron? --- p.16 / Biological versus artificial neuron --- p.16 / Operation of a neural network --- p.17 / Neural network paradigm --- p.20 / Feedforward as the most suitable form of neural network --- p.22 / Capability of neural network --- p.23 / The learning process --- p.25 / Testing the network --- p.29 / Neural network computing --- p.29 / Neural network versus conventional computer --- p.30 / Neural network versus a knowledge based system --- p.32 / Strength of neural network --- p.34 / Weaknesses of neural network --- p.35 / Chapter V. --- NEURAL NETWORK AS A TOOL FOR INVESTMENT ANALYSIS --- p.38 / Neural network in financial applications --- p.38 / Trading in the stock market --- p.41 / Why neural network could outperform in the stock market? --- p.43 / Applications of neural network --- p.45 / Chapter VI. --- BUILDING THE NEURAL NETWORK MODEL --- p.47 / Implementation process --- p.48 / Step 1´ؤ Problem specification --- p.49 / Step 2 ´ؤ Data collection --- p.51 / Step 3 ´ؤ Data analysis and transformation --- p.55 / Step 4 ´ؤ Training data set extraction --- p.58 / Step 5 ´ؤ Selection of network architecture --- p.60 / Step 6 ´ؤ Selection of training algorithm --- p.62 / Step 7 ´ؤ Training the network --- p.64 / Step 8 ´ؤ Model deployment --- p.65 / Chapter 7 --- RESULT AND CONCLUSION --- p.67 / Result --- p.67 / Conclusion --- p.69 / BIBLIOGRAPHY --- p.72

Wang, Xiaolei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 34-39). / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Identification of Stock Market Crashes --- p.5 / Chapter 2.1 --- Identification Criteria --- p.7 / Chapter 2.2 --- Identification Results --- p.8 / Chapter Chapter 3. --- Data --- p.10 / Chapter 3.1 --- Data Issue for Chapter 4 --- p.10 / Chapter 3.2 --- Data Issue for Chapter 5 --- p.12 / Chapter 3.3 --- Data Issue for Chapter 6 --- p.12 / Chapter Chapter 4. --- Examination of AFE --- p.13 / Chapter 4.1 --- Definition of AFE and MAAFE --- p.13 / Chapter 4.2 --- Examination of MAAFE --- p.14 / Chapter 4.3 --- Examination of AFE by Grouping Duration --- p.15 / Chapter Chapter 5. --- Examination of AFD --- p.18 / Chapter Chapter 6. --- Examination of the Relationship between AFD and ESR --- p.22 / Chapter 6.1 --- Portfolio Strategy - Sorting by Size and Dispersion --- p.23 / Chapter 6.2 --- Portfolio Strategy - Sorting by Size and Book to Market Ratio --- p.26 / Chapter 6.3 --- Fama-French Time Series Regression Test (Three-Factor Model) --- p.28 / Chapter 6.4 --- Fama-French Time Series Regression Test (Three-Factor Model with Dispersion on the Right Hand Side) --- p.30 / Chapter 6.5 --- Introduction of a Nonlinear Form of AFD to the Fama-French Model --- p.31 / Chapter Chapter 7. --- Conclusions --- p.32 / References --- p.34 / Appendix Table I to Table XVI --- p.40-55 / Figure I to Figure VI --- p.56-61