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.
Keywords
Stock market index prediction,Deep learning,Deep Neural Network,Stock index,BIST
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