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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

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

January 1999 (has links)
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

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