Predicting the Turkish Stock Market BIST 30 Index using Deep Learning

Year 2019, Volume: 11 Issue: 1, 253 - 265, 31.01.2019

Halil Raşo , Mehmet Demirci

https://doi.org/10.29137/umagd.425560

Cited By: 9

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

References

• Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y. & Ngo, D. C. L. (2015). Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Elsevier, Expert Systems with Applications, 42(1), 306-324.
• Shynkevich, Y., McGinnity, T.M., Coleman, S. & Belatreche, A. (2015). Stock price prediction based on stock-specific and sub-industry-specific news articles. IEEE, Neural Networks (IJCNN), 12-17.
• Oliveir, N., Cortez, P. & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Elsevier, Expert Systems with Applications, 73, 125-144.
• Ni, Z., Wang, D. & Xue, W. (2015). Investor sentiment and its nonlinear effect on stock returns—New evidence from the Chinese stock market based on panel quantile regression model. Elsevier, Economic Modelling, 50, 266-274.
• Leigh, W., Purvis, R. & Ragusa, J. M. (2002). Forecasting the NYSE Composite Index with Technical Analysis, Pattern Recognizer, Neural Network, and Genetic Algorithm: A Case Study in Romantic Decision Support. Elsevier, Decision Support Systems, 32, 361-377.
• Gui, B., Wei, X., Shen, Q. Qi, J. & Guo, L. (2014). Financial Time Series Forecasting Using Support Vector Machine. IEEE, CIS, 15-16.
• Dechow, P. M., Hutton, A. P., Meulbroek, L. & Sloan, R. G. (2001). Short-sellers, fundamental analysis, and stock returns. Elsevier, Journal of Financial Economics, 61(1), 77-106.
• Lewellen, J. (2010). Accounting anomalies and fundamental analysis: An alternative view. Elsevier, Journal of Accounting and Economics, 50(2-3), 455-466. Dechow, P., Ge, W. & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Elsevier, Journal of Accounting and Economics, 50(2-3), 344-40.
• Qian, B. & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Springer, Applied Intelligence, 26(1), 25–33.
• Sands, T. M., Tayal, D., Morris, M. E. & Monteiro, S. T. (2015). Robust stock value prediction using support vector machines with particle swarm optimization. IEEE, Evolutionary Computation (CEC).
• Ince, H. & Trafalis, T. B. (2017). A Hybrid forecasting model for stock market prediction. Economic Computation and Economic Cybernetics Studies and Research, 51(3).
• Bastı, E., Kuzey, C. & Delen, D. (2015). Analyzing initial public offerings' short-term performance using decision trees and SVMs, 73, 15-27.
• Chen, Y. & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Elsevier, Expert Systems with Applications, 80, 340-355.
• Teixeira, L. A. & De Oliveira, A. L. I. (2010). A method for automatic stock trading combining technical analysis and nearest neighbor classification. Elsevier, Expert Systems with Applications, 37(10), 6885-6890.
• Zhang, Y. & Wu, L. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Elsevier, Expert Systems with Applications, 36(5), 8849-8854.
• Moghaddam, A. H., Moghaddam, M. H. & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Elsevier, Journal of Economics, Finance and Administrative Science, 21(41), 89-93.
• Boyacioglu, M. A. & Avci, D. (2010). An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Elsevier, Expert Systems with Applications, 37(12), 7908-7912.
• Akita, R., Yoshihara, A., Matsubara, T. & Uehara, K. (2016). Deep learning for stock prediction using numerical and textual information. IEEE, Computer and Information Science (ICIS).
• Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Elsevier, Expert Systems with Applications, 42, 2162–2172.
• Sakarya, Ş., Yavuz, M., Karaoğlan, A. D. & Özdemir, N. (2015). Stock market index prediction with neural network during financial crises: A review on Bist-100. 1, 2, 53-67.