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