Optimization of trading strategy by genetic algorithms and wavelet transform with an Internet application to the Hong Kong stock option

Pao Yue-kong Library Electronic Theses Database

Optimization of trading strategy by genetic algorithms and wavelet transform with an Internet application to the Hong Kong stock option

 

Author: Lee, Kai-leung
Title: Optimization of trading strategy by genetic algorithms and wavelet transform with an Internet application to the Hong Kong stock option
Degree: M.Sc.
Year: 1999
Subject: Stock options -- China -- Hong Kong -- Decision making -- Data processing
Investments -- China -- Hong Kong -- Decision making -- Data processing
Time-series analysis -- Data processing
Internet
Expert systems (Computer science)
Genetic algorithms
Wavelets (Mathematics)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: v, 106 leaves : ill. ; 31 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1471935
URI: http://theses.lib.polyu.edu.hk/handle/200/317
Abstract: Financial information such as news, quotations of financial instruments, and economic data, etc., is critical to making investment decisions. However, access to these information may require additional system facilities and membership rights. Yet, a trader may need to extract only those useful information for analysis before arriving at a desired trading strategy. One important class of financial information is financial time series. Financial time series often exhibits irregularity or spikes. Also, it is time-varying and non-linear in the distribution of price variation. Analysis usually applies to multiple financial series. With such model complexities and the cumbersome of information extraction of large amount of data periodically, the aim of this dissertation attempts to develop an Internet application to assist general traders for making investment decisions by optimizing trading strategy in terms of profit based on the data collected and extracted from the Internet. The problem domain is the Hong Kong Stock Option. Genetic Algorithms are computer simulated optimization search methodology inspired by natural evolutionary process and environmental survival. It is used to optimize the search for optimal strategy for stock option trading. Developing trading strategy requires modeling of the market conditions. The behavior of financial time series provides this information. Financial time series often exhibits outliers like short transients or spikes. Wavelet transform is useful in its ability to localize data in time-series and capture short lived transients to give better model accuracy. This dissertation aims at developing a stock option trading expert system to provide optimal trading strategies by applying genetic algorithms to explore optimality over a set of option trading rules and wavelet transform to capture high frequencies behavior in financial time series. The resulted trading strategy is searched with the goal of maximizing profit and minimizing risk. Hong Kong stock option and stock daily closing traded records are available in the Internet. This availability makes adaptive optimization possible for the changing market.

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