Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/39111
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dc.contributor.authorNg, Wee Ding.en_US
dc.date.accessioned2010-05-21T04:44:01Z-
dc.date.available2010-05-21T04:44:01Z-
dc.date.copyright2006en_US
dc.date.issued2006-
dc.identifier.urihttp://hdl.handle.net/10356/39111-
dc.description69 p.en_US
dc.description.abstractStock Selection using GGAP-RBF (General Growing and Pruning Radial Basis Function) Neural Network In the stock picking or selection problem, the motivation is to design system such that it is able to systematically select stocks or equities that are empirically predicted to perform well in the future. Thus, it should achieve four objectives: 1) to achieve monetary returns in the future; 2) to beat the average market performance; 3) to avoid the bias in human-based stock selection due to psychological reasons; 4) automatically crunching the data to extract useful information based on past information, which can be hardly done manually. Hence, this dissertation presents methodologies to select equities based on soft-computing models and the information gathered from fundamental analysis of stock performance.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Applications of electronicsen_US
dc.titleStock selection using General Growing and Pruning Radial Basis Function (GGAP-RBF) neural networken_US
dc.typeThesisen_US
dc.contributor.supervisorQuah Tong Sengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Communication Software and Networks)en_US
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