Abstract
Predicting stock prices accurately is a key goal of investors in the stock market. Unfortunately, stock prices are constantly changing and affected by many factors, making the process of predicting them a challenging task. This paper describes a method to build models for predicting stock prices using long short-term memory network (LSTM). The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly augmented data to predict future stock prices. We evaluate the proposed method using data sets of four stocks. The results show that the proposed method outperforms others in predicting stock prices based on different performance metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Malkiel, B.G., Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25(2), 383–417 (1970)
Miao, K., Chen, F., Zhao, Z.-G.: Stock price forecast based on bacterial colony RBF neural network. J. Qingdao Univ. (Nat. Sci. Ed.) 2, 011 (2007)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31, 307–327 (1986)
Box, G.E., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken (2015)
Hadavandi, E., Ghanbari, A., Abbasian-Naghneh, S.: Developing an evolutionary neural network model for stock index forecasting. In: Huang, D.-S., McGinnity, M., Heutte, L., Zhang, X.-P. (eds.) ICIC 2010. CCIS, vol. 93, pp. 407–415. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14831-6_54
Jain, S., Dr. Roopam, G., Dr. Asmita A.M., Stock price prediction on daily stock data. Int. J. Neural Netw. Adv. Appl., 5 (2018)
Göçken, M., Özçalıcı, M., Boru, A., Dosdoğru, A.T.: Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst. Appl. 44, 320–331 (2016)
Kim, K.-J., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst. Appl. 19, 125–132 (2000)
Lendasse, A., de Bodt, E., Wertz, V., Verleysen, M.: Non-linear financial time series forecasting - application to the Bel 20 stock market index. Eur. J. Econ. Soc. Syst. 14(1), 81–91 (2000)
Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42, 2162–2172 (2014)
Ticknor, J.L.: A bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40, 5501–5506 (2013)
Chen, K., Zhou, Y., Dai, F.: A LSTM-based method for stock returns prediction: a case study of China stock market, In: 2015 IEEE International Conference on Big Data (Big Data) (2015)
Nelson, D.M.Q., Pereira, A.C.M., Oliveira, R.A.d.: Stock market’s price movement prediction with LSTM neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN) (2017)
Chiang, W.-C., Enke, D., Wu, T., Wang, R.: An adaptive stock index trading decision support system. Expert Syst. Appl. 59, 195–207 (2016)
Baek, Y., Kim, H.Y.: ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst. Appl. 113, 457–480 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
LeCunn, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series, The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)
Selvin, S.: Stock price prediction using LSTM, RNN and CNN-sliding window model, In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2017)
Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39, 43–62 (1997)
Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks, In: ICLR 2016 (2016)
Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling, In: INTERSPEECH 2014 (2014)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling, In: INTERSPEECH 2012 (2012)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28, 2222–2232 (2017)
Finance, Y.: Yahoo! Finance. https://finance.yahoo.com/
Nasdaq, “Nasdaq”. https://www.nasdaq.com/
Keras, “Keras”. https://pypi.org/project/Keras/
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: The 3rd International Conference for Learning Representations, San Diego (2014)
Kara, Y., Acar Boyacioglu, M., Baykan, Ö.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert syst. Appl. 38, 5311–5319 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, D.H.D., Tran, L.P., Nguyen, V. (2019). Predicting Stock Prices Using Dynamic LSTM Models. In: Florez, H., Leon, M., Diaz-Nafria, J., Belli, S. (eds) Applied Informatics. ICAI 2019. Communications in Computer and Information Science, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-32475-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-32475-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32474-2
Online ISBN: 978-3-030-32475-9
eBook Packages: Computer ScienceComputer Science (R0)