Stock portfolio optimization and risk management with genetic algorithms

Pao Yue-kong Library Electronic Theses Database

Stock portfolio optimization and risk management with genetic algorithms

 

Author: Wang, Xu
Title: Stock portfolio optimization and risk management with genetic algorithms
Degree: M.Sc.
Year: 2011
Subject: Hong Kong Polytechnic University -- Dissertations
Stocks
Genetic algorithms
Department: Dept. of Computing
Pages: ix, 95 leaves : ill. ; 30 cm.
InnoPac Record: http://library.polyu.edu.hk/record=b2426867
URI: http://theses.lib.polyu.edu.hk/handle/200/6029
Abstract: Modern Portfolio Theory is established on Harry Markowitz's 1952 thesis about the Mean-Variance portfolios. He presented that a rational investor should either maximize the expected return for a given level of risk, or minimize the risk for a given level of expected return. Associated with the Efficient Market Hypothesis, these two investment principles lead to the Efficient Portfolios as well as the Capital Market Line when the risk is free. Almost sixty years later, extensive volumes of new approaches have been applied in the research on Mean-Variance Portfolio Theory like the Technical Indicators and all kinds of investment software with the help of Artificial Intelligence Techniques like Fuzzy Logic, Neural Network and Expert Systems. But there are no widely accepted practical implementations based on the theory and its ideal condition. The purpose of this Master Dissertation is to develop a robust portfolio optimization model, concentrating on the fundamental fields of stock investment. The design aims to generate optimized portfolios and test the effectiveness of Efficient Market Hypothesis and the financial strategy of "Riding the Bull, Beating the Bear". Maximize its investment performance, by searching an optimal stock portfolio and holding period selected from the 45 constituent stocks of the Hang Seng Index over 4 different spans, which last for 1900 trading days in 7 years, with the measurement of an objective function by applying Genetic Algorithms. Finally, the subsequent optimization results will be optimized.

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