Price discovery of stock index with informationally-linked markets using artificial neural network.

• Other Authors: Ng, Wai-Leung Anthony.
• Format: Others
• Language: English; Chinese
• Published: 1999
• Subjects: Stock price forecasting > Data processing; Stock price forecasting > China > Hong Kong > Data processing; Stock index futures > Data processing; Stock index futures > China > Hong Kong > Data processing; Neural networks (Computer science)
• Online Access: http://library.cuhk.edu.hk/record=b5889930; http://repository.lib.cuhk.edu.hk/en/item/cuhk-322846

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