Classification Of BIST -100 Index’ Changes Via Machine Learning Methods

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Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi
Volume:39 Issue:1
Classification Of BIST -100 Index’ Changes Via Machine Learning Methods

Classification Of BIST -100 Index’ Changes Via Machine Learning Methods

Authors : Enes FİLİZ, Ersoy ÖZ

Pages : 117-129

Doi:10.14780/muiibd.329913

View : 42 | Download : 18

Publication Date : 2017-07-19

Article Type : Research Paper

Abstract :The changes in BIST-100 index are economically crucial. In this study, classifications will be made with the assumption that the changes in BIST-100 index are dependent on certain factors. The classifiers to be used are k-nearest neighbor algorithm, naive Bayes Classifier, logistic regression and C4.5 classifier from the machine learning methods. Factors affecting the change of BIST-100 index values are deemed as Euro/ Dollar Parity, Gold value insert ignore into journalissuearticles values(ounce);, Crude Oil Prices, Monthly Interest Rates, Inflation Data and DAX, FTSE, S&P 500 that are widely used in the literature. As a result of the transactions performed via Weka program, the most successful methods in order are C4.5 classifier algorithm insert ignore into journalissuearticles values(66.2%); and logistic regression analysis insert ignore into journalissuearticles values(65.9%);.
Keywords : BIST 100 Index, Machine Learning Methods, Classification

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