Predicting the Turkish Stock Market BIST 30 Index using Deep Learning

Abstract

The non-linearity and high change rates of stock market index prices make prediction a challenging problem for traders and data scientists. Data modeling and machine learning have been extensively utilized for proposing solutions to this difficult problem. In recent years, deep learning has proved itself in solving such complex problems. In this paper, we tackle the problem of forecasting the Turkish Stock Market BIST 30 index movements and prices. We propose a deep learning model fed with technical indicators and oscillators calculated from historical index price data. Experiments conducted by applying our model on a dataset gathered for a period of 27 months on www.investing.com demonstrate that our solution outperforms other similar proposals and attains good accuracy, achieving 0.0332, 0.109, 0.09, 0.1069 and 0.2581 as mean squared error in predicting BIST 30 index prices for the next five trading days. Based on these results, we argue that using deep neural networks is advisable for stock market index prediction.