Long-term prediction in Tehran stock market using a new architecture of Deep neural networks

Document Type : Case Study

Authors

1 Department of control and systems, K. N. Toosi University of technology, Tehran,Iran

2 دانشگاه صنعتی خواجه نصیرالدین طوسی

Abstract

Financial markets play an important role in the economy of modern societies. Therefore, many researchers have investigated to forecast these markets using various statistical and soft computing methods. Financial time series are essentially complex, dynamic, nonlinear, noisy, nonparametric, and chaotic, so they cannot be described by analytical equations, because their dynamics are too complex or unknown. In recent years, deep learning methods have attracted lots of attention, due to their exceptional performance compared to other existing approaches in many learning problems. The objective of this paper is long-term prediction of price time series in Tehran Stock Exchange. For this purpose, a new architecture of two deep learning methods, Long-Short Term Memory (LSTM) and Recurrent Neural Network (RNN), for ten-step ahead simultaneous prediction, are proposed. That is a multivariable structure with multi outputs. By using the output error feedbacks as internal inputs, the network can learn error dynamics during the training phase. Experimental results show the high capability of the proposed structure for both methods in multi-step ahead stock price forecasting and the superiority of the LSTM network compared to RNN for long-term predictions.

Keywords

Main Subjects

References

[1]Rajabi, M., & Khaloozadeh, H. (2018, May). Investigation and Comparison of the Performance of Multi-Objective Evolutionary Algorithms Based on Decomposition and Dominance in Portfolio Optimization. In Electrical Engineering (ICEE), Iranian Conference on (pp. 923-929). IEEE.
[2]Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346-14355.
[3]Shah, D., Isah, H., & Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. International Journal of Financial Studies, 7(2), 26.
 [4]Martinez, L. C., da Hora, D. N., Palotti, J. R. D. M., Meira, W., & Pappa, G. L. (2009, June). From an artificial neural network to a stock market day-trading system: A case study on the bm&f bovespa. In 2009 International Joint Conference on Neural Networks (pp. 2006-2013). IEEE.
[5]Chen, J. (2010, October). SVM application of financial time series forecasting using empirical technical indicators. In 2010 International Conference on Information, Networking and Automation (ICINA) (Vol. 1, pp. V1-77). IEEE.
[6]Barak, S., Arjmand, A., & Ortobelli, S. (2017). Fusion of multiple diverse predictors in stock market. Information Fusion, 36, 90-102.
 [7]Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702.
[8]Gheyas, I. A., & Smith, L. S. (2011). A novel neural network ensemble architecture for time series forecasting. Neurocomputing, 74(18), 3855-3864.
[9]Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
[10]Zhu, B., & Chevallier, J. (2017). Carbon price forecasting with a hybrid Arima and least squares support vector machines methodology. In Pricing and Forecasting Carbon Markets (pp. 87-107). Springer, Cham.
[11]Nayak, R. K., Mishra, D., & Rath, A. K. (2015). A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Applied Soft Computing, 35, 670-680.
[12]Iqbal, Z., Ilyas, R., Shahzad, W., Mahmood, Z., & Anjum, J. (2013). Efficient machine learning techniques for stock market prediction. International Journal of Engineering Research and Applications, 3(6), 855-867.
[13]Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
[14]Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
[15]Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research. IEEE computational intelligence magazine, 5(4), 13-18.
[16]LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
[17]Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
[18]Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
[19]Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008, July). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning (pp. 1096-1103). ACM.
[20]Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
[21]Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[22] Khaloozadeh, H., & Sedigh, A. K. (2001, July). Long term prediction of Tehran price index (TEPIX) using neural networks. In Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) (Vol. 1, pp. 563-567). IEEE.
[23]Siami-Namini, S., & Namin, A. S. (2018). Forecasting economics and financial time series: Arima vs. lstm. arXiv preprint arXiv:1803.06386.
[24]Nelson, D. M., Pereira, A. C., & de Oliveira, R. A. (2017, May). Stock market's price movement prediction with LSTM neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 1419-1426). IEEE.
[25] Althelaya, K. A., El-Alfy, E. S. M., & Mohammed, S. (2018, April). Evaluation of bidirectional lstm for short-and long-term stock market prediction. In 2018 9th international conference on information and communication systems (ICICS) (pp. 151-156). IEEE.
[26]Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569-18584.
 [27]Shen, F., Chao, J., & Zhao, J. (2015). Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 167, 243-253.
[28]Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47-56.
[29]Takeuchi, L., & Lee, Y. Y. A. (2013). Applying deep learning to enhance momentum trading strategies in stocks. In Technical Report. Stanford University.
[30]Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental pollution, 231, 997-1004.
[31] Lei, J., Liu, C., & Jiang, D. (2019). Fault diagnosis of wind turbine based on Long Short-term memory networks. Renewable energy, 133, 422-432.
[32]Yuan, C., Xiu, T., & Lou, T. (2019, April). Probabilistic Long-term Load Forecasting Based on Stacked LSTM. In Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence (pp. 80-84).
[33]Wang, Y., Liu, M., Bao, Z., & Zhang, S. (2018). Short-term load forecasting with multi-source data using gated recurrent unit neural networks. Energies, 11(5), 1138.
[34]Farnaghi, M & Rahimi, H. (2017). Spatio-Temporal Prediction of Monthly Rainfall using Deep Neural Network: A Case Study in North-west Iran. JGST. 6(4), 123-142. (in Persian)
[35]Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma, Technische Universität München, 91(1).
[36]Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
[37]Luu, Q., Nguyen, S., & Pham, U. (2020). Time series prediction: A combination of Long Short-Term Memory and structural time series models. Science & Technology Development Journal-Economics-Law and Management, 4(1), 500-515.
[38]Yuan, X., Li, L., & Wang, Y. (2019). Nonlinear dynamic soft sensor modeling with supervised long short-term memory network. IEEE Transactions on Industrial Informatics.
[39]Quigley, L., & Ramsey, D. (2008). Statistical analysis of the log returns of financial assets. Financial mathematic, University of Limerick, 32.
[40]Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.6980.
AUT Journal of Modeling and Simulation
Volume 52, Issue 2
December 2020
Pages 179-188

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