Learning to Fuse Multiple Semantic Aspects from Rich Texts for Stock Price Prediction

  • Ning Tang
  • Yanyan ShenEmail author
  • Junjie Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


Stock price prediction is challenging due to the non-stationary fluctuation of stock price, which can be influenced by the stochastic trading behaviors in the market. In recent years, researchers have focused on exploiting massive text data such news and tweets to predict stock price, achieving promising outcomes. Existing methods typically compress each text into a fixed-length representation vector, whereas rich texts may involve multiple semantic aspect-level information that has different effects on the future stock price. In this paper, we propose a novel Multi-head Attention Fusion Network (MAFN) to exploit aspect-level semantic information from texts to enhance prediction performance. MAFN employs the encoder-decoder framework, where the encoder adopts the multi-head attention mechanism to automatically learn the aspect-level text representations via different attention heads. Furthermore, we subtly fuse the learned representations by discarding the dross and selecting the essential. The decoder generates stock price sequence by incorporating textual information and historical price dynamically via the hierarchical attention. Experimental results on real data sets show the superior performance of MAFN against several strong baselines as well as the effectiveness of exploiting and fusing fine-grained aspect-level textual information for stock price prediction.


Stock price prediction Multi-head attention Encoder-decoder 



This work is supported by the National Key Research and Development Program of China (No. 2018YFC0831604). Yanyan Shen is also supported by NSFC (No. 61602297). Junjie Yao is supported by NSFC 61502169, U1509219 and SHEITC.


  1. 1.
    Adam, K., Marcet, A., Nicolini, J.P.: Stock market volatility and learning. J. Finan. 71(1), 33–82 (2016)CrossRefGoogle Scholar
  2. 2.
    Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)Google Scholar
  3. 3.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  4. 4.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  5. 5.
    Chung, J., Ahn, S., Bengio, Y.: Hierarchical multiscale recurrent neural networks. In: International Conference on Learning Representations (2017)Google Scholar
  6. 6.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1415–1425 (2014)Google Scholar
  7. 7.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)Google Scholar
  8. 8.
    Feng, F., Chen, H., He, X., Ding, J., Sun, M., Chua, T.S.: Improving stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936 (2018)
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Hu, Z., Liu, W., Bian, J., Liu, X., Liu, T.Y.: Listening to chaotic whispers: a deep learning framework for news-oriented stock trend prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 261–269. ACM (2018)Google Scholar
  11. 11.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  12. 12.
    Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  13. 13.
    Li, Q., Wang, T., Li, P., Liu, L., Gong, Q., Chen, Y.: The effect of news and public mood on stock movements. Inf. Sci. 278, 826–840 (2014)CrossRefGoogle Scholar
  14. 14.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)Google Scholar
  15. 15.
    Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
  16. 16.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  17. 17.
    Preethi, G., Santhi, B.: Stock market forecasting techniques: a survey. J. Theor. Appl. Inf. Technol. 46(1), 24–30 (2012)Google Scholar
  18. 18.
    Si, J., Mukherjee, A., Liu, B., Pan, S.J., Li, Q., Li, H.: Exploiting social relations and sentiment for stock prediction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1139–1145 (2014)Google Scholar
  19. 19.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)Google Scholar
  21. 21.
    Tabari, N., Seyeditabari, A., Peddi, T., Hadzikadic, M., Zadrozny, W.: A comparison of neural network methods for accurate sentiment analysis of stock market tweets. In: Alzate, C., et al. (eds.) MIDAS/PAP -2018. LNCS (LNAI), vol. 11054, pp. 51–65. Springer, Cham (2019). Scholar
  22. 22.
    Tao, C., Gao, S., Shang, M., Wu, W., Zhao, D., Yan, R.: Get the point of my utterance! learning towards effective responses with multi-head attention mechanism. In: IJCAI, pp. 4418–4424 (2018)Google Scholar
  23. 23.
    Xu, Y., Cohen, S.B.: Stock movement prediction from tweets and historical prices. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1970–1979 (2018)Google Scholar
  24. 24.
    Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4694–4702 (2015)Google Scholar
  25. 25.
    Zhang, L., Aggarwal, C.C., Qi, G.: Stock price prediction via discovering multi-frequency trading patterns, pp. 2141–2149 (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina

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