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2019 | OriginalPaper | Buchkapitel

Predicting Stock Prices Using Dynamic LSTM Models

verfasst von : Duc Huu Dat Nguyen, Loc Phuoc Tran, Vu Nguyen

Erschienen in: Applied Informatics

Verlag: Springer International Publishing

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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.

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Malkiel, B.G., Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25(2), 383–417 (1970)CrossRef

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)MathSciNetCrossRef

Box, G.E., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken (2015)MATH

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_54CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

10.

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)CrossRef

11.

Ticknor, J.L.: A bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40, 5501–5506 (2013)CrossRef

12.

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)

13.

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)

14.

Chiang, W.-C., Enke, D., Wu, T., Wang, R.: An adaptive stock index trading decision support system. Expert Syst. Appl. 59, 195–207 (2016)CrossRef

15.

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)CrossRef

16.

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

17.

LeCunn, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series, The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)

18.

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)

19.

Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39, 43–62 (1997)CrossRef

20.

Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks, In: ICLR 2016 (2016)

21.

Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling, In: INTERSPEECH 2014 (2014)

22.

Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling, In: INTERSPEECH 2012 (2012)

23.

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)MathSciNetCrossRef

24.

Finance, Y.: Yahoo! Finance. https://finance.yahoo.com/

25.

Nasdaq, “Nasdaq”. https://www.nasdaq.com/

26.

Keras, “Keras”. https://pypi.org/project/Keras/

27.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: The 3rd International Conference for Learning Representations, San Diego (2014)

28.

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)CrossRef

Titel: Predicting Stock Prices Using Dynamic LSTM Models
verfasst von: Duc Huu Dat Nguyen
Loc Phuoc Tran
Vu Nguyen
Verlag: Springer International Publishing
Buch: Applied Informatics
Print ISBN: 978-3-030-32474-2

Electronic ISBN: 978-3-030-32475-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32475-9_15

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