Indicator selection with committee decision of filter methods for stock market price trend in ISE

Abstract

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 prediction of the next day direction with reduced dataset has an improvement over the prediction of it with full dataset. © 2016 Elsevier B.V.