Stock Market Trend Prediction Using Recurrent Convolutional Neural Networks

  • Bo Xu
  • Dongyu Zhang
  • Shaowu Zhang
  • Hengchao Li
  • Hongfei LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


Short-term prediction of stock market trend has potential application for personal investment without high-frequency-trading infrastructure. Existing studies on stock market trend prediction have introduced machine learning methods with handcrafted features. However, manual labor spent on handcrafting features is expensive. To reduce manual labor, we propose a novel recurrent convolutional neural network for predicting stock market trend. Our network can automatically capture useful information from news on stock market without any handcrafted feature. In our network, we first introduce an entity embedding layer to automatically learn entity embedding using financial news. We then use a convolutional layer to extract key information affecting stock market trend, and use a long short-term memory neural network to learn context-dependent relations in financial news for stock market trend prediction. Experimental results show that our model can achieve significant improvement in terms of both overall prediction and individual stock predictions, compared with the state-of-the-art baseline methods.


Stock market prediction Embedding layer Convolutional neural network Long short-term memory 



This work is partially supported by grant from the Natural Science Foundation of China (No. 61632011, 61572102, 61702080, 61602079, 61562080), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002), the Fundamental Research Funds for the Central Universities.


  1. 1.
    Huang, W., Nakamori, Y., Wang, S.Y.: Forecasting stock market movement direction with support vector machine. Comput. Oper. Res. 32(10), 2513–2522 (2005)CrossRefGoogle Scholar
  2. 2.
    Lee, M.C.: Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst. Appl. 36(8), 10896–10904 (2009)CrossRefGoogle Scholar
  3. 3.
    Ni, L.P., Ni, Z.W., Gao, Y.Z.: Stock trend prediction based on fractal feature selection and support vector machine. Expert Syst. Appl. 38(5), 5569–5576 (2011)CrossRefGoogle Scholar
  4. 4.
    Yu, L., Wang, S., Lai, K.K.: Mining stock market tendency using GA-based support vector machines. In: Deng, X., Ye, Y. (eds.) WINE 2005. LNCS, vol. 3828, pp. 336–345. Springer, Heidelberg (2005). Scholar
  5. 5.
    Chai, J., Du, J., Lai, K.K., et al.: A hybrid least square support vector machine model with parameters optimization for stock forecasting. Math. Probl. Eng. 2015, 1–7 (2015)CrossRefGoogle Scholar
  6. 6.
    Marković, I., Stojanović, M., Božić, M., Stanković, J.: Stock market trend prediction based on the LS-SVM model update algorithm. In: Bogdanova, A.M., Gjorgjevikj, D. (eds.) ICT Innovations 2014. AISC, vol. 311, pp. 105–114. Springer, Cham (2015). Scholar
  7. 7.
    Yu, L., Chen, H., Wang, S., et al.: Evolving least squares support vector machines for stock market trend mining. IEEE Trans. Evol. Comput. 13(1), 87–102 (2009)CrossRefGoogle Scholar
  8. 8.
    Crone, S.F., Kourentzes, N.: Feature selection for time series prediction – a combined filter and wrapper approach for neural networks. Neurocomputing 73(10), 1923–1936 (2010)CrossRefGoogle Scholar
  9. 9.
    Dai, W., Wu, J.Y., Lu, C.J.: Combining Nonlinear Independent Component Analysis and Neural Network for the Prediction of Asian Stock Market Indexes. Pergamon Press Inc., Tarrytown (2012)Google Scholar
  10. 10.
    Kara, Y., Acar Boyacioglu, M., Baykan, Ö.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines. Expert Syst. Appl. 38(5), 5311–5319 (2011)CrossRefGoogle Scholar
  11. 11.
    Kogan, S., Levin, D., Routledge, B.R., et al.: Predicting risk from financial reports with regression. In: North American Chapter of the Association for Computational Linguistics, pp. 272–280 (2009)Google Scholar
  12. 12.
    Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using financial news articles. In: Americas Conference on Information Systems (2006)Google Scholar
  13. 13.
    Hsieh, T.J., Hsiao, H.F., Yeh, W.C.: Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Appl. Soft Comput. 11(2), 2510–2525 (2011)CrossRefGoogle Scholar
  14. 14.
    Ding, X., Zhang, Y., Liu, T., et al.: Deep learning for event-driven stock prediction. In: Ijcai, pp. 2327–2333 2015Google Scholar
  15. 15.
    dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp. 69–78 (2014)Google Scholar
  16. 16.
    Xie, B., Passonneau, R.J., Wu, L., Creamer, G.G.: Semantic frames to predict stock price movement. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 873–883 (2013)Google Scholar
  17. 17.
    Martin, L., Lars, K., Amy, L.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42, 11–24 (2014)CrossRefGoogle Scholar
  18. 18.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. In: EMNLP, pp. 1415–1425 (2014)Google Scholar
  19. 19.
    Schmidhuber, J., Hochreiter, S.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  20. 20.
    Guo, C., Berkhahn, F.: Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 (2016)
  21. 21.
    Tetlock, P.C., Saar Tsechansky, M., Macskassy, S.: More than words: quantifying language to measure firms’ fundamentals. J. Finance 63(3), 1437–1467 (2008)CrossRefGoogle Scholar
  22. 22.
    Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: Proceedings of the 21st International Conference on World Wide Web, pp. 909–918. ACM (2012)Google Scholar
  23. 23.
    Luss, R., d’Aspremont, A.: Predicting abnormal returns from news using text classification. Quant. Finance 15(6), 999–1012 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence (No. EPFL-CONF-192344) (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bo Xu
    • 1
  • Dongyu Zhang
    • 1
  • Shaowu Zhang
    • 1
  • Hengchao Li
    • 1
  • Hongfei Lin
    • 1
    Email author
  1. 1.School of Computer Science and TechnologyDalian University of TechnologyDalianChina

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