Stock portoflio [i.e. portfolio] optimization using genetic algorithm

Pao Yue-kong Library Electronic Theses Database

Stock portoflio [i.e. portfolio] optimization using genetic algorithm


Author: Wong, Shing-yue Samuel
Title: Stock portoflio [i.e. portfolio] optimization using genetic algorithm
Degree: M.Sc.
Year: 2002
Subject: Hong Kong Polytechnic University -- Dissertations
Stock price forecasting -- Mathematical models
Genetic algorithms
Department: Dept. of Computing
Pages: [165] leaves : ill. ; 30 cm.
InnoPac Record:
Abstract: Lots of literature on stock portfolio optimization by using artificial intelligence has been written. Most of them are based on time series tracking and factors model. The use of artificial intelligence on mean variance theorem is seldom encountered. Given the simple and elegant mean variance theorem, stock portfolio optimization can be approached in a new perspective by using expected return of stock over a period and its corresponding standard deviation as a yardstick for the measurement of stock portfolio performance. The purpose of the study is to search for the optimal combination of weights in a selected basket of stock to maximize its performance. By using the yardsticks of mean variance theory as a yardstick of measurement, the portfolio performance is compared against the performance of Hang Seng index. After due consideration on the alternatives on artificial intelligence tools, neural network, expert systems, fuzzy systems and genetic algorithm, the application of genetic algorithm for this type of parallel search process is considered suitable. The Matlab Genetic Algorithm Optimization Toolbox (GAOT) is used to conduct the analysis. The study is divided into two parts. The first part is to use genetic algorithm base on the pass data to find the optimal stock portfolio that outperforms Hang Seng index in terms of higher rate of return with lower risk. The result is positive. The second part makes use of the selected weight of the optimal portfolio in the result of part one to make projections on future return in the next investment period. The result reveals a positive projected return that is highly correlated to the pass return where it is based. But the projected return is lower than the pass return and only projected return on longer periods (over 5 days) outperforms Hang Seng Index.

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