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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.contributor.advisorChi, Zheru George (EIE)en_US
dc.creatorZhou, Wenming-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleOptimization of investment strategies using evolutionary computationen_US
dcterms.abstractThe aim of this dissertation is to provide a method for developing optimized investment strategies using genetic programming. Previous work show that additional economic fundamentals data put into the model used for developing investment strategies can lead to improved results. For most prediction models based on AI or machine learning technology, too much input data may lead to dimension disaster. Genetic programming (GP) is a kind of evolution computation technology which can find optimized investment strategies by searching for combinations of economic factors. Previous studies have shown that it becomes extremely inefficient in computing when the number of factors increase. GP is seldom used in existing applications to develop optimized investment strategies based on massive indicators of economic fundamentals. In order to make use of the evolutionary algorithm to obtain better investment strategy, this dissertation proposed the idea of using nonlinear kernel function to greatly reduce the number of factors. We introduced the clustering algorithm and principal component analysis both are based on kernel function. Genetic programming algorithm are adopted to work on a small number of variables to search for strategies. In order to verify the effectiveness of our method, rule model of investment strategy is defined, and experiments are conducted using the genetic programming. The rule to maximize the revenue by buying and selling index funds was developed. Our experiments demonstrate that the nonlinear dimensionality reduction method based on Gaussian kernel function can effectively reduce the number of economic indicators, thus breaking the limit that GP algorithm can only work effectively on combining a small number of factors. The strategy found by GP algorithm run well on test data by gaining a compound annualized rate of 6.5% in mainland stock market of china, which can provide a useful reference for development of investment strategies.en_US
dcterms.extentx, 54 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHInvestments -- Mathematicsen_US
dcterms.LCSHInvestment analysis -- Mathematicsen_US
dcterms.LCSHGenetic programming (Computer science)en_US
dcterms.LCSHGenetic algorithmsen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/11186