dc.contributor.authorYe, Shuhong
dc.date.accessioned2017-05-23T08:10:11Z
dc.date.available2017-05-23T08:10:11Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/72032
dc.description.abstractThis paper is mainly focused on the academic goal. By reproducing the method in the existing published paper using the same data sets and recommend improvements on the procedure. Two main feature selection methods are used in this paper. One is the Classic Support Vector Machine regression and another one is an improved method Feature-weighted Support Vector Machine regression which was proposed by James N. K. Liu and Yanxing Hu. The improved method combines the Classic Support Vector Machine with the Grey correlation degree. Given different weight values to different features, the closer the relation between the feature to the target problem, the higher the weight value will be given. The processed data then goes into the Support Vector Machine regression training and the output model will be used for forecasting the stock daily close price. In this paper, the historical data and technical indicators of 7 stocks are downloaded from China Shenzhen A-share market. 5 stocks are used same as the reference data in the same period. Another 2 Growth Enterprise Market stocks are added to test generalization of the method. The result for this paper shows the Feature-weighted Support Vector Machine regression has better performance in forecasting the stock price.en_US
dc.format.extent43 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleFeature selection methods for financial engineeringen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US


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