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

Özet

The changes in BIST-100 index are economically crucial. In this study, classifications will be made withthe assumption that the changes in BIST-100 index are dependent on certain factors. The classifiers to beused are k-nearest neighbor algorithm, naive Bayes Classifier, logistic regression and C4.5 classifier fromthe machine learning methods. Factors affecting the change of BIST-100 index values are deemed as Euro/Dollar Parity, Gold value (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 Wekaprogram, the most successful methods in order are C4.5 classifier algorithm (66.2%) and logistic regressionanalysis (65.9%).