Abstract
In order to test the predictive power of the deep learning model, several machine learning methods were introduced for comparison. Empirical case results for the period of 2000 to 2017 show the forecasting power of deep learning technology. With a series of linear regression indicator measurement, we find LSTM networks outperform traditional machine learning methods, i.e., Linear Regression, Auto ARIMA, KNN.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finance 25(2), 383–417 (1970)
Wang, B., Huang, H., Wang, X.: A novel text mining approach to financial time series forecasting. Neurocomputing 83(6), 136–145 (2012)
Caginalp, G., Laurent, H.: The predictive power of price patterns. Appl. Math. Finance 5(3–4), 181–205 (1998)
Marshall, B.R., Young, M.R., Rose, L.C.: Candlestick technical trading strategies: can they create value for investors? J. Bank. Finance 30(8), 2303–2323 (2006)
Burr, T.: Pattern recognition and machine learning. J. Am. Stat. Assoc. 103(482), 886–887 (2008)
Das, S.P., Padhy, S.: Support vector machines for prediction of futures prices in indian stock market. Int. J. Comput. Appl. 41(3), 22–26 (2013)
Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Decis. Support Syst. 47(2), 115–125 (2009)
Refenes, A.N., Zapranis, A., Francis, G.: Stock performance modeling using neural networks: a comparative study with regression models. Neural Netw. 7(2), 375–388 (1994)
Guo, Z., Wang, H., Liu, Q., Yang, J.: A feature fusion based forecasting model for financial time series. PLoS One 9(6), 1–13 (2014)
Zhu, C., Yin, J., Li, Q.: A stock decision support system based on DBNs. J. Comput. Inf. Syst. 10(2), 883–893 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2012)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Bengio, Y., Lamblin, P., Dan, P., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153–160 (2007)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Cavalcante, R.C., Brasileiro, R.C., Souza, V.L.F., Nobrega, J.P., Oliveira, A.L.I.: Computational intelligence and financial markets: a survey and future directions. Expert Syst. Appl. 55, 194–211 (2016)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: International Conference on Artificial Intelligence, pp. 2327–2333 (2015)
Dixon, M.F., Klabjan, D., Bang, J.: Implementing deep neural networks for financial market prediction on the Intel Xeon Phi, vol. 101, no. 8, pp. 1–6. Social Science Electronic Publishing (2015)
Sirignano, J.: Deep Learning for Limit Order Books. Social Science Electronic Publishing, Rochester (2016)
Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Plos One 12(7), e0180944 (2017)
Cao, J., Li, Z., Li, J.: Financial time series forecasting model based on CEEMDAN and LSTM. Phys. A 519, 127–139 (2019)
Krauss, C., Xuan, A.D., Huck, N.: Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. Eur. J. Oper. Res. 259(2), 689–702 (2016)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013)
Olah, C.: Understanding LSTM networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed 31 Mar 2019
Palangi, H., Ward, R., Deng, L.: Distributed compressive sensing: a deep learning approach. IEEE Trans. Signal Process. 64(17), 4504–4518 (2016)
Sak, H., Senior, A., Beaufays, F.: Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. Comput. Sci., 338–342 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cakra, Y.E., Trisedya, B.D.: Stock price prediction using linear regression based on sentiment analysis. In: International Conference on Advanced Computer Science & Information Systems, pp. 147–153 (2015)
Nie, C.-X., Song, F.-T.: Analyzing the stock market based on the structure of KNN network. Chaos, Solitons Fractals 113, 148–159 (2018)
Pai, P.F., Lin, C.S.: A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6), 497–505 (2005)
Acknowledgments
This research was partially supported by National Natural Science Foundation of China (Grant No. 71771006 and 71771008) and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, Y., Liu, S., Yang, H., Wu, H. (2020). A Deep Learning Framework for Stock Prediction Using LSTM. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics: Complexity of Decisions and Decisions for Complexity. DECON 2019. Advances in Intelligent Systems and Computing, vol 1009. Springer, Cham. https://doi.org/10.1007/978-3-030-38227-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-38227-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38226-1
Online ISBN: 978-3-030-38227-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)