A Stock Market Prediction Model Based on Deep Learning Networks

الموضوعات :

seyyedeh mozhgan Beheshti Masalegou ¹ , Mohammad-Ali Afshar-Kazemie ² , jalal haghighat monfared ³ , Ali Rezaeian ⁴

1 - Department of Information Technology Managment,Tehran Central Branch,Islamic Azad Univrsity,Tehran,Iran
2 - Department of Industrial Management, Tehran Central Branch, Islamic Azad University, Tehran, Iran
3 - Department Of Industrial Management, Tehran Central Branch ,Islamic Azad University , Tehran , Iran
4 - Department of Governmental Management, Faculty of Management and Accounting ,Shahid Beheshti University, Tehran, Iran

تاريخ الإرسال : 29 الأربعاء , رجب, 1443 تاريخ التأكيد : 16 الأحد , محرم, 1444 تاريخ الإصدار : 07 الخميس , جمادى الأولى, 1444

الکلمات المفتاحية: Long-Short term memory Autoencoder (LSTM-AE), Dimensionality Reduction, deep learning, Stock market prediction,

ملخص المقالة :

Accurate stock market prediction can assist in an efficient portfolio and risk management. However, accurately predicting stock price trends still is an elusive goal, not only because the stock market is affected by policies, market environment, and market sentiment, but also because stock price data is inherently complex, noisy, and nonlinear. Recently, the rapid development of deep learning can make the classifiers more robust, which can be used to solve nonlinear problems. This study proposes a hybrid framework using Long Short-Term Memory, Autoencoder, and Deep Neural Networks (LSTM-AE-DNNs). Specifically, LSTM-AE is responsible for extracting relevant features, and in order to predict price movement, the features are fed into two deep learning models based on a recurrent neural network (RNN) and multilayer perceptron (MLP). The dataset used for this is Dow Jones daily stock for 2008-2018, which was used in this article. Besides, to further assess the prediction performance of the proposed model, original stock features are fed to the single RNN and MLP models. The results showed that the proposed model gives the more accurate and best results compared to another. In particular, LSTM-AE+RNN shows a better performance than the LSTM-AE+MLP. In addition, hybrid models show better performance compared to a single DNN fed with the all-stock features directly.

المصادر:

Albahli, S., Nazir, T., Mehmood, A., Irtaza, A., Alkhalifah, A., & Albattah, W. (2022). AEI-DNET: a novel densenet model with an autoencoder for the stock market predictions using stock technical indicators. Electronics, 11(4), 611. https://doi.org/10.3390/electronics11040611

Bachelier, L. (1900). Théorie de la spéculation. Annales scientifiques de l'École normale supérieure,

Baek, Y., & Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, 457-480. https://doi.org/10.1016/j.eswa.2018.07.019

Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), e0180944. https://doi.org/10.1371/journal.pone.0180944

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006

Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 100320. https://doi.org/10.1016/j.mlwa.2022.100320

Box, G., Jenkins, G., Reinsel, G., & Ljung, G. (2015). Time series analysis, control, and forecasting . Hoboken. In: New Jersey: John Wiley & Sons. https://doi.org/10.1111/jtsa.12194

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211. https://doi.org/10.1016/j.eswa.2016.02.006

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1), 30-42. https://doi.org/10.1109/TASL.2011.2134090

Dietrich, R., Opper, M., & Sompolinsky, H. (1999). Statistical mechanics of support vector networks. Physical review letters, 82(14), 2975. https://doi.org/10.1103/PhysRevLett.82.2975

Doğan, S., Koçak, D., & Atan, M. (2022). Financial Distress Prediction Using Support Vector Machines and Logistic Regression. In Advances in Econometrics, Operational Research, Data Science and Actuarial Studies (pp. 429-452). Springer. https://doi.org/10.1007/978-3-030-85254-2_26

Fama, E. F. (2021). Market efficiency, long-term returns, and behavioral finance. University of Chicago Press. 10.7208/9780226426983-019

Gensler, A., Henze, J., Sick, B., & Raabe, N. (2016). Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks. 2016 IEEE international conference on systems, man, and cybernetics (SMC), https://doi.org/10.1109/SMC.2016.7844673

Gunduz, H. (2021). An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination. Financial Innovation, 7(1), 1-24. https://doi.org/10.1186/s40854-021-00243-3

Gündüz, H., Çataltepe, Z., & Yaslan, Y. (2017). Stock daily return prediction using expanded features and feature selection. Turkish Journal of Electrical Engineering & Computer Sciences, 25(6), 4829-4840. 10.3906/elk-1704-256

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of finance and data science, 4(3), 183-201. https://doi.org/10.1016/j.jfds.2018.04.003

Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., & Sainath, T. N. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97. https://doi.org/10.1109/MSP.2012.2205597

Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351-1362. https://doi.org/10.1016/j.procs.2018.05.050

Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. https://doi.org/10.1016/j.eswa.2019.03.029

Jin, Z., Yang, Y., & Liu, Y. (2019). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 1-17. https://doi.org/10.1007/s00521-019-04504-2

Jung, G., & Choi, S.-Y. (2021). Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques. Complexity, 2021. https://doi.org/10.1155/2021/6647534

Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. 2014 science and information conference, https://doi.org/10.1109/SAI.2014.6918213

Kou, G., Yang, P., Peng, Y., Xiao, F., Chen, Y., & Alsaadi, F. E. (2020). Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods. Applied Soft Computing, 86, 105836. https://doi.org/10.1016/j.asoc.2019.105836

Lin, F.-L., Yang, S.-Y., Marsh, T., & Chen, Y.-F. (2018). Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics & Finance, 55, 285-294. https://doi.org/10.1016/j.iref.2017.07.013

Modjtahedi, A., & Daneshvar, A. (2020). A New Credit Risk System Using Hybrid ELECTRE TRI and NSGA-II Methods. Journal of System Management, 6(4), 1-25. https://dx.doi.org/10.30495/jsm.2021.1924341.1445

Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89-93. https://doi.org/10.1016/j.jefas.2016.07.002

Nelson, D. M., Pereira, A. C., & de Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. 2017 International joint conference on neural networks (IJCNN), https://doi.org/10.1109/IJCNN.2017.7966019

Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (2020). A survey on machine learning for stock price prediction: algorithms and techniques. http://dx.doi.org/10.5220/0009340700630071

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268. https://doi.org/10.1016/j.eswa.2014.07.040

Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PloS one, 15(1), e0227222. https://doi.org/10.1371/journal.pone.0227222

Rather, A. M., Sastry, V., & Agarwal, A. (2017). Stock market prediction and Portfolio selection models: a survey. Opsearch, 54(3), 558-579. https://doi.org/10.1007/s12597-016-0289-y

Sagheer, A., & Kotb, M. (2019). Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems. Scientific reports, 9(1), 1-16. https://doi.org/10.1038/s41598-019-55320-6

Salmani Danglani, S., Saeedi, P., Bahramzadeh, H. A., & Pourshahabi, F. (2019). Representing the Pattern of Relationship between Personality Traits and Investment Patterns in the Stock Market. Journal of System Management, 5(1), 79-114. https://dorl.net/dor/20.1001.1.23222301.2019.5.1.5.6

Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 1-12. https://doi.org/10.1186/s40854-019-0131-7

Srivastava, A. K., Srivastava, A., Singh, S., Sugandha, S., & Gupta, S. (2022). Design of Machine-Learning Classifier for Stock Market Prediction. SN Computer Science, 3(1), 1-11. https://doi.org/10.1007/s42979-021-00970-5

Taheri, A., Shafiee, M., & Evazzadeh Fath, F. (2019). Investigating the Role of Non-Financial Information Analysis and Risk-Return Analysis along with Financial Information in Increasing the Efficiency of the Stock Portfolio of Banks. Journal of System Management, 5(3), 123-138. https://dorl.net/dor/20.1001.1.23222301.2019.5.3.8.3

Xu, Y., Chhim, L., Zheng, B., & Nojima, Y. (2020). Stacked deep learning structure with bidirectional long-short term memory for stock market prediction. International Conference on Neural Computing for Advanced Applications, https://doi.org/10.1007/978-981-15-7670-6_37

Yadav, A., Jha, C., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100. https://doi.org/10.1016/j.procs.2020.03.257

Yan, Y., & Yang, D. (2021). A stock trend forecast algorithm based on deep neural networks. Scientific Programming, 2021. https://doi.org/10.1155/2021/7510641

Yang, J., Zhang, S., Xiang, Y., Liu, J., Liu, J., Han, X., & Teng, F. (2020). LSTM auto‐encoder based representative scenario generation method for hybrid hydro‐PV power system. IET Generation, Transmission & Distribution, 14(24), 5935-5943. https://doi.org/10.1049/iet-gtd.2020.0757

Yu, P., & Yan, X. (2020). Stock price prediction based on deep neural networks. Neural Computing and Applications, 32(6), 1609-1628. https://doi.org/10.1007/s00521-019-04212-x

Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139. https://doi.org/10.1016/j.eswa.2016.09.027

Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20. https://doi.org/10.1186/s40854-019-0138-0