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dc.contributorFaculty of Engineeringen_US
dc.creatorWu, Shanlei-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/5971-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleGP-based financial market forecasten_US
dcterms.abstractGenetic programming is a technique to automatically discover computer programs using the principles of Darwinian evolution. Since its inception, genetic programming has been used to solve many practical problems, producing a number of human competitive results and even patentable new inventions. This dissertation first presents the theory of genetic programming, and introduces its basic process for solving problems. Then we explain the fundamental idea of technical analysis in stock trading and discuss some important technical indicators that can be used in price prediction with genetic programming. In the experiments, we use the historical data together with the technical indicators extracted from the data for the price prediction with genetic programming. Hang Seng Index (HSI) between 2000 and 2008 are used as the training data and different window sizes are investigated. As a comparison, other computational intelligence methods including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back Propagation (BP) network, and Genetic Algorithm (GA) are also evaluated on the same data. Experimental results show the genetic programming can produce a comparable performance with some attractive features.en_US
dcterms.extent67 leaves : ill. (some col.) ; 30 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2011en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.LCSHGenetic programming (Computer science)en_US
dcterms.LCSHStock price forecastingen_US
dcterms.LCSHStock exchanges -- Forecastingen_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/200/5971