by Kwok-Fai Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 100-103). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.4 / Chapter 1.1 --- Summary of Contributions --- p.5 / Chapter 1.2 --- Organization of the Thesis --- p.6 / Chapter 2 --- Current Methods and Problems --- p.8 / Chapter 2.1 --- Statisticians --- p.8 / Chapter 2.1.1 --- ARMA --- p.8 / Chapter 2.1.1.1 --- Moving Average models --- p.8 / Chapter 2.1.1.2 --- Autoregressive models --- p.9 / Chapter 2.1.1.3 --- Stationary Process --- p.10 / Chapter 2.1.1.4 --- Autoregressive-Moving Average model --- p.10 / Chapter 2.1.1.5 --- Parameter Estimation --- p.11 / Chapter 2.2 --- Financial Researchers --- p.11 / Chapter 2.2.1 --- Efficient Market Theory --- p.11 / Chapter 2.3 --- Computer Scientists --- p.12 / Chapter 2.3.1 --- Expert System --- p.12 / Chapter 2.3.2 --- Neural Network --- p.14 / Chapter 2.3.2.1 --- Multilayer Perceptron --- p.14 / Chapter 2.3.2.2 --- Radial Basis Function Network (RBF) --- p.19 / Chapter 2.4 --- Research Apart from Prediction and Trading in Finance --- p.22 / Chapter 2.4.1 --- Derivatives Valuation and Hedging --- p.22 / Chapter 2.4.1.1 --- Volatility --- p.22 / Chapter 2.4.2 --- Pricing of Initial Public Offering --- p.24 / Chapter 2.4.3 --- Credit Rating --- p.25 / Chapter 2.4.4 --- Financial Health Assessment --- p.26 / Chapter 2.5 --- Discussion --- p.27 / Chapter 3 --- Correlation Basis Function Network --- p.28 / Chapter 3.1 --- Formulation of CBF network --- p.31 / Chapter 3.2 --- First Order Learning Algorithm --- p.32 / Chapter 3.3 --- Summary --- p.35 / Chapter 4 --- Applications of CBF Network in Stock trading --- p.36 / Chapter 4.1 --- Data Representation --- p.36 / Chapter 4.2 --- Data Pre-processing --- p.38 / Chapter 4.2.1 --- Input data pre-processing --- p.38 / Chapter 4.2.2 --- Output data pre-processing --- p.38 / Chapter 4.3 --- Multiple CBF Networks for Generation of Trading Signals --- p.41 / Chapter 4.4 --- Output Data Post-processing --- p.41 / Chapter 4.5 --- Trader's Interpretation --- p.43 / Chapter 4.6 --- Maximum profit trading system --- p.45 / Chapter 4.7 --- Performance Evaluation --- p.46 / Chapter 5 --- Applications of CBF Network in Warrant trading --- p.48 / Chapter 5.1 --- Option Model --- p.48 / Chapter 5.2 --- Warrant Model --- p.49 / Chapter 5.3 --- Black-Scholes Pricing Formula --- p.51 / Chapter 5.4 --- Using CBF Network for choosing warrants --- p.53 / Chapter 5.5 --- Trading System --- p.53 / Chapter 5.5.1 --- Trading System by Black-Scholes Model --- p.54 / Chapter 5.5.2 --- Trading System by Warrant Sensitivity --- p.55 / Chapter 5.6 --- Learning of Parameters in Warrant Sensitivity Model by Hierarchi- cal CBF Network --- p.57 / Chapter 5.7 --- Experimental Results --- p.59 / Chapter 5.7.1 --- Aggregate profit --- p.62 / Chapter 5.8 --- Summary and Discussion --- p.69 / Chapter 6 --- Analysis of CBF Network and other models --- p.72 / Chapter 6.1 --- Time and Space Complexity --- p.72 / Chapter 6.1.1 --- RBF Network --- p.72 / Chapter 6.1.2 --- CBF Network --- p.74 / Chapter 6.1.3 --- Black-Scholes Pricing Formula --- p.74 / Chapter 6.1.4 --- Warrant Sensitivity Model --- p.75 / Chapter 6.2 --- "Model Confidence, Prediction Confidence and Model Stability" --- p.76 / Chapter 6.2.1 --- Model and Prediction Confidence --- p.77 / Chapter 6.2.2 --- Model Stability --- p.77 / Chapter 6.2.3 --- Linear Model Analysis --- p.79 / Chapter 6.2.4 --- CBF Network Analysis --- p.82 / Chapter 6.2.5 --- Black-Scholes Pricing Formula Analysis --- p.84 / Chapter 7 --- Conclusion --- p.93 / Chapter 7.1 --- Neural Network and Statistical Modeling --- p.95 / Chapter 7.2 --- Financial Markets --- p.95 / Chapter A --- RBF Network Parameters Estimation --- p.101 / Chapter A.1 --- Least Squares --- p.101 / Chapter A.2 --- Gradient Descent Algorithm --- p.103 / Chapter B --- Further study on Black-Scholes Model --- p.104

Kei-Keung Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 127-128). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Static Portfolio Techniques --- p.1 / Chapter 1.2 --- Neural Network Approach --- p.2 / Chapter 1.3 --- Contributions of this Thesis --- p.3 / Chapter 1.4 --- Application of this Research --- p.4 / Chapter 1.5 --- Organization of this Thesis --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Standard Markowian Portfolio Optimization (SMPO) and Sharpe Ratio --- p.6 / Chapter 2.2 --- Downside Risk --- p.9 / Chapter 2.3 --- Augmented Lagrangian Method --- p.10 / Chapter 2.4 --- Adaptive Supervised Learning Decision (ASLD) System --- p.13 / Chapter I --- Static Portfolio Optimization Techniques --- p.19 / Chapter 3 --- Modified Portfolio Sharpe Ratio Maximization (MPSRM) --- p.20 / Chapter 3.1 --- Experiment Setting --- p.21 / Chapter 3.2 --- Downside Risk and Upside Volatility --- p.22 / Chapter 3.3 --- Investment Diversification --- p.24 / Chapter 3.4 --- Analysis of the Parameters H and B of MPSRM --- p.27 / Chapter 3.5 --- Risk Minimization with Control of Expected Return --- p.30 / Chapter 3.6 --- Return Maximization with Control of Expected Downside Risk --- p.32 / Chapter 4 --- Variations of Modified Portfolio Sharpe Ratio Maximization --- p.34 / Chapter 4.1 --- Soft-max Version of Modified Portfolio Sharpe Ratio Maximization (SMP- SRM) --- p.35 / Chapter 4.1.1 --- Applying Soft-max Technique to Modified Portfolio Sharpe Ratio Maximization (SMPSRM) --- p.35 / Chapter 4.1.2 --- Risk Minimization with Control of Expected Return --- p.37 / Chapter 4.1.3 --- Return Maximization with Control of Expected Downside Risk --- p.38 / Chapter 4.2 --- Soft-max Version of MPSRM with Entropy-like Regularization Term (SMPSRM-E) --- p.39 / Chapter 4.2.1 --- Using Entropy-like Regularization term in Soft-max version of Modified Portfolio Sharpe Ratio Maximization (SMPSRM-E) --- p.39 / Chapter 4.2.2 --- Risk Minimization with Control of Expected Return --- p.41 / Chapter 4.2.3 --- Return Maximization with Control of Expected Downside Risk --- p.43 / Chapter 4.3 --- Analysis of Parameters in SMPSRM and SMPSRM-E --- p.44 / Chapter II --- Neural Network Approach --- p.48 / Chapter 5 --- Investment on a Foreign Exchange Market using ASLD system --- p.49 / Chapter 5.1 --- Investment on A Foreign Exchange Portfolio --- p.49 / Chapter 5.2 --- Two Important Issues Revisited --- p.51 / Chapter 6 --- Investment on Stock market using ASLD System --- p.54 / Chapter 6.1 --- Investment on Hong Kong Hang Seng Index --- p.54 / Chapter 6.1.1 --- Performance of the Original ASLD System --- p.54 / Chapter 6.1.2 --- Performances After Adding Several Heuristic Strategies --- p.55 / Chapter 6.2 --- Investment on Six Different Stock Indexes --- p.61 / Chapter 6.2.1 --- Structure and Operation of the New System --- p.62 / Chapter 6.2.2 --- Experimental Results --- p.63 / Chapter III --- Combination of Static Portfolio Optimization techniques with Neural Network Approach --- p.67 / Chapter 7 --- Combining the ASLD system with Different Portfolio Optimizations --- p.68 / Chapter 7.1 --- Structure and Operation of the New System --- p.69 / Chapter 7.2 --- Combined with the Standard Markowian Portfolio Optimization (SMPO) --- p.70 / Chapter 7.3 --- Combined with the Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.72 / Chapter 7.4 --- Combined with the MPSRM ´ؤ Risk Minimization with Control of Ex- pected Return --- p.74 / Chapter 7.5 --- Combined with the MPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.76 / Chapter 7.6 --- Combined with the Soft-max Version of MPSRM (SMPSRM) --- p.77 / Chapter 7.7 --- Combined with the SMPSRM - Risk Minimization with Control of Ex- pected Return --- p.79 / Chapter 7.8 --- Combined with the SMPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.80 / Chapter 7.9 --- Combined with the Soft-max Version of MPSRM with Entropy-like Reg- ularization Term (SMPSRM-E) --- p.82 / Chapter 7.10 --- Combined with the SMPSRM-E ´ؤ Risk Minimization with Control of Expected Return --- p.84 / Chapter 7.11 --- Combined with the SMPSRM-E ´ؤ Return Maximization with Control of Expected Downside Risk --- p.86 / Chapter IV --- Software Developed --- p.93 / Chapter 8 --- Windows Application Developed --- p.94 / Chapter 8.1 --- Decision on Platform and Programming Language --- p.94 / Chapter 8.2 --- System Design --- p.96 / Chapter 8.3 --- Operation of our program --- p.97 / Chapter 9 --- Conclusion --- p.103 / Chapter A --- Algorithm for Portfolio Sharpe Ratio Maximization (PSRM) --- p.105 / Chapter B --- Algorithm for Improved Portfolio Sharpe Ratio Maximization (ISRM) --- p.107 / Chapter C --- Proof of Regularization Term Y --- p.109 / Chapter D --- Algorithm for Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.111 / Chapter E --- Algorithm for MPSRM with Control of Expected Return --- p.113 / Chapter F --- Algorithm for MPSRM with Control of Expected Downside Risk --- p.115 / Chapter G --- Algorithm for SMPSRM with Control of Expected Return --- p.117 / Chapter H --- Algorithm for SMPSRM with Control of Expected Downside Risk --- p.119 / Chapter I --- Proof of Entropy-like Regularization Term --- p.121 / Chapter J --- Algorithm for SMPSRM-E with Control of Expected Return --- p.123 / Chapter K --- Algorithm for SMPSRM-E with Control of Expected Downside Riskl25 Bibliography --- p.127

Lam Lui-fuk. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 75-76). / Abstracts in English and Chinese. / ABSTRACT --- p.I / 摘要 --- p.II / ACKNOWLEDGEMENT --- p.III / TABLE OF CONTENTS --- p.IV / LIST OF FIGURES --- p.VIII / LIST OF ABBREVIATIONS --- p.X / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2. --- Motivation and Research Objective --- p.3 / Chapter 1.3. --- Organization of the Dissertation --- p.3 / Chapter CHAPTER 2 --- BACKGROUND STUDIES --- p.5 / Chapter 2.1 --- The Contract Net Protocol --- p.5 / Chapter 2.2 --- Two-tier software architectures --- p.8 / Chapter 2.3 --- Three-tier software architecture --- p.9 / Chapter CHAPTER 3 --- SYSTEM ARCHITECTURE --- p.12 / Chapter 3.1 --- Introduction --- p.12 / Chapter 3.2 --- System Architecture Overview --- p.12 / Chapter 3.2.1 --- Client Layer --- p.13 / Chapter 3.2.2 --- Middle Layer --- p.13 / Chapter 3.2.3 --- Back-end Layer --- p.14 / Chapter 3.3 --- Advantages of the System Architecture --- p.14 / Chapter 3.3.1 --- "Separate the presentation components, business logic and data storage" --- p.14 / Chapter 3.3.2 --- Provide a central-computing platform for user using different computing platforms --- p.15 / Chapter 3.3.3 --- Improve system capacity --- p.15 / Chapter 3.3.4 --- Enable distributed computing --- p.16 / Chapter CHAPTER 4. --- SOFTWARE ARCHITECTURE --- p.17 / Chapter 4.1 --- Introduction --- p.17 / Chapter 4.2 --- Descriptions of Middle Layer Server Side Software Components --- p.17 / Chapter 4.2.1 --- Data Cache --- p.18 / Chapter 4.2.2 --- Functions Library --- p.18 / Chapter 4.2.3 --- Communicator --- p.18 / Chapter 4.2.4 --- Planner Module --- p.19 / Chapter 4.2.5 --- Scheduler module --- p.19 / Chapter 4.2.6 --- Execution Module --- p.20 / Chapter 4.3 --- Overview the Execution of Service Request inside Server --- p.20 / Chapter 4.4 --- Descriptions of Client layer Software Components --- p.21 / Chapter 4.4.1 --- Graphical User Interface --- p.22 / Chapter 4.5 --- Overview of Task Execution in Advanced Client ´ةs Application --- p.23 / Chapter 4.6 --- The possible usages of task allocation protocol --- p.24 / Chapter 4.6.1 --- Chart Drawing --- p.25 / Chapter 4.6.2 --- Compute user-defined technical analysis indicator --- p.25 / Chapter 4.6.3 --- Unbalance loading --- p.26 / Chapter 4.6.4 --- Large number of small data mining V.S. small number of large data mining --- p.26 / Chapter 4.7 --- Summary --- p.27 / Chapter CHAPTER 5. --- THE CONTRACT NET PROTOCOL FOR TASK ALLOCATION --- p.28 / Chapter 5.1 --- Introduction --- p.28 / Chapter 5.2 --- The FIPA Contract Net Interaction Protocol --- p.28 / Chapter 5.2.1 --- Introduction to the FIPA Contract Net Interaction Protocol --- p.28 / Chapter 5.2.2 --- Strengths of the FIPA Contract Net Interaction Protocol for our system --- p.30 / Chapter 5.2.3 --- Weakness of the FIPA Contractor Net Interaction Protocol for our system --- p.32 / Chapter 5.3 --- The Modified Contract Net Protocol --- p.33 / Chapter 5.4 --- The Implementation of the Modified Contract Net Protocol --- p.39 / Chapter 5.5 --- Summary --- p.46 / Chapter CHAPTER 6. --- A CLIENT AS SERVER MODEL USING MCNP FOR TASK ALLOCATION --- p.48 / Chapter 6.1 --- Introduction --- p.48 / Chapter 6.2 --- The CASS System Model --- p.48 / Chapter 6.3 --- The analytical model of the CASS system --- p.51 / Chapter 6.4 --- Performance Analysis of the CASS System --- p.55 / Chapter 6.5 --- Performance Simulation --- p.62 / Chapter 6.6 --- An Extension of the Load-Balancing Algorithm for Non-Uniform Client's Service Time Distribution --- p.68 / Chapter 6.7 --- Summary --- p.69 / Chapter CHAPTER 7. --- CONCLUSION AND FUTURE WORK --- p.71 / Chapter 7.1 --- Conclusion --- p.71 / Chapter 7.2 --- Future Work --- p.73 / BIBLIOGRAPHY --- p.75

Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of trading rules in financial markets, and for each class of trading rule, PRS includes various combinations of the rule parameters to produce a universe of 1059 component trading rules in all. Each component rule is assigned a starting weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to the large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. By omitting the traditional reduce phase of MapReduce, the proposed parallel PSO avoids the I/O cost of intermediate data and gets higher speedup ratio than previous parallel PSO based on MapReduce. After being optimized in an eight years training period, PRS is tested on an out-of-sample data set. The experimental results show that PRS outperforms all of the component rules in the testing period. / published_or_final_version / Computer Science / Master / Master of Philosophy

South African home users of the Internet use the Internet to perform various everyday functions. These functions include, but are not limited to, online shopping, online gaming, social networking and online banking. Home users of online banking face multiple threats, such as phishing and social engineering. These threats come from hackers attempting to obtain confidential information, such as online banking authentication credentials, from home users. It is, thus, essential that home users of online banking be made aware of these threats, how to identify them and what countermeasures to implement to protect themselves from hackers. In this respect, information security awareness (ISA) programmes are an effective way of making the home users of online banking aware of both the threats they face and the countermeasures available to protect themselves from these threats. There are certain legal requirements with which South African banks have to comply when implementing ISA initiatives. Non-compliance or failure to demonstrate due care and due diligence should a security incident occur will result in financial penalties for the bank as well as possible brand damage and loss of customers. Banks implement international best practice recommendations in an effort to comply with legislation. These include recommendations for information security awareness. This research investigated both information security best practice recommendations and information security legal requirements for information security awareness. A selected list of information security best practices was investigated for best practice recommendations while a selected list of information security legislation was investigated for legal requirements imposed on South African banks. A gap analysis was performed on both these recommendations and requirements to determine whether the implementation of best practice recommendations resulted in compliance with legal requirements. The gap analysis found that the implementation of best practice recommendations does not result in compliance with legal requirements. Accordingly, the outcome of this research highlighted the importance of understanding the legal requirements and ensuring that adequate controls are in place with which to achieve compliance. / Business Information systems / Msc. (Information systems)

South African home users of the Internet use the Internet to perform various everyday functions. These functions include, but are not limited to, online shopping, online gaming, social networking and online banking. Home users of online banking face multiple threats, such as phishing and social engineering. These threats come from hackers attempting to obtain confidential information, such as online banking authentication credentials, from home users. It is, thus, essential that home users of online banking be made aware of these threats, how to identify them and what countermeasures to implement to protect themselves from hackers. In this respect, information security awareness (ISA) programmes are an effective way of making the home users of online banking aware of both the threats they face and the countermeasures available to protect themselves from these threats. There are certain legal requirements with which South African banks have to comply when implementing ISA initiatives. Non-compliance or failure to demonstrate due care and due diligence should a security incident occur will result in financial penalties for the bank as well as possible brand damage and loss of customers. Banks implement international best practice recommendations in an effort to comply with legislation. These include recommendations for information security awareness. This research investigated both information security best practice recommendations and information security legal requirements for information security awareness. A selected list of information security best practices was investigated for best practice recommendations while a selected list of information security legislation was investigated for legal requirements imposed on South African banks. A gap analysis was performed on both these recommendations and requirements to determine whether the implementation of best practice recommendations resulted in compliance with legal requirements. The gap analysis found that the implementation of best practice recommendations does not result in compliance with legal requirements. Accordingly, the outcome of this research highlighted the importance of understanding the legal requirements and ensuring that adequate controls are in place with which to achieve compliance. / Business Information systems / Msc. (Information systems)