Indicator selection with committee decision of filter methods for stock market price trend in ISE
作者: Ayça Çakmak PehlivanlıBarış AşıkgilGüzhan Gülay
作者单位: 1Mimar Sinan Fine Arts University, Faculty of Science and Letters, Department of Statistics, Room: 503, Bomonti Campus, Şişli, 34380 İstanbul, Turkey
2Borsa İstanbul, Emirgan, 34467 İstanbul, Turkey
刊名: Applied Soft Computing, 2016, Vol.49 , pp.792-800
来源数据库: Elsevier Journal
DOI: 10.1016/j.asoc.2016.09.004
关键词: Stock market priceSupport vector machinesFeature selectionFilter methodsIstanbul Stock Exchange
原始语种摘要: Abstract(#br)Prediction of the stock market price direction is a challenging and important task of the financial time series. This study presents the prediction of the next day stock price direction by the optimal subset indicators selected with ensemble feature selection approach. The main focus is to obtain the final best feature subset which also yields good prediction of the next day price trend by removing irrelevant and redundant indicators from the dataset. For this purpose, filter methods are combined, support vector machines (SVM) has been carried out and finally voting scheme is applied. In order to conduct these processes, a real dataset obtained from Istanbul Stock Exchange (ISE) is used with technical and macroeconomic indicators. The result of this study shows that the...
全文获取路径: Elsevier  (合作)
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影响因子:2.14 (2012)

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