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

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

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

This thesis consists of three parts: a genetic fuzzy trading model for stock trading, incremental intraday information for financial time series forecasting, and intraday effects in conditional variance estimation. Part A investigates a genetic fuzzy trading model for stock trading. This part contributes to use a fuzzy trading model to eliminate undesirable discontinuities, incorporate vague trading rules into the trading model and use genetic algorithm to select an optimal trading ruleset. Technical indicators are used to monitor the stock price movement and assist practitioners to set up trading rules to make buy-sell decision. Although some trading rules have a clear buy-sell signal, the signals are always detected with 'hard' logical. These trigger the undesirable discontinuities due to the jumps of the Boolean variables that may occur for small changes of the technical indicator. Some trading rules are vague and conflicting. They are difficult to incorporate into the trading system while they possess significant market information. Various performance comparisons such as total return, maximum drawdown and profit-loss ratios among different trading strategies were examined. Genetic fuzzy trading model always gave moderate performance. Part B studies and contributes to the literature that focuses on the forecasting of daily financial time series using intraday information. Conventional daily forecast always focuses on the use of lagged daily information up to the last market close while neglecting intraday information from the last market close to current time. Such intraday information are referred to incremental intraday information. They can improve prediction accuracy not only at a particular instant but also with the intraday time when an appropriate predictor is derived from such information. These are demonstrated in two forecasting examples, predictions of daily high and range-based volatility, using linear regression and Neural Network forecasters. Neural Network forecaster possesses a stronger causal effect of incremental intraday information on the predictand. Predictability can be estimated by a correlation without conducting any forecast. Part C explores intraday effects in conditional variance estimation. This contributes to the literature that focuses on conditional variance estimation with the intraday effects. Conventional GARCH volatility is formulated with an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance. However, the intra-daily information doesn't include in the conditional variance while it should has implication on the daily variance. Using Engle's multiplicative-error model formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The impact of significant changes in intraday data is reflected in the MEM-GARCH variance. For some frameworks, it is possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance equation. / Ng, Hoi Shing Raymond. / Adviser: Kai-Pui Lam. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 107-114). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

by Ng Wai-Leung Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6 / Chapter 2.2 --- Importance of Index Forecasting --- p.6 / Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9 / Chapter 2.4 --- Importance of the lead-lag relationship --- p.10 / Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10 / Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12 / Chapter 2.7 --- Artificial Neural Network Architecture --- p.14 / Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20 / Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25 / Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28 / Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29 / Chapter IV. --- METHODOLOGY --- p.37 / Chapter 4.1 --- ADF Test for Unit Root --- p.38 / Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38 / Chapter 4.3 --- Comparison Criteria of Different Models --- p.39 / Chapter 4.4 --- Data Analysis --- p.39 / Chapter 4.5 --- Data Manipulations --- p.41 / Chapter V. --- RESULTS --- p.42 / Chapter 5.1 --- The Resulting Models --- p.42 / Chapter 5.2 --- The Prediction Power among the Models --- p.45 / Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46 / Chapter VI. --- CAUSALITY ANALYSIS --- p.54 / Chapter 6.1 --- Granger Casuality Analysis --- p.55 / Chapter 6.2 --- Results Interpretation --- p.56 / Chapter VII --- CONSISTENCE VALIDATION --- p.61 / Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67 / Chapter 7.1 --- Trading System Architecture --- p.68 / Chapter 7.2 --- Simulation Runs using the Trading System --- p.77 / Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79