Exploiting Twitter Moods to Boost Financial Trend Prediction Based on Deep Network Models

  • Yifu Huang
  • Kai Huang
  • Yang Wang
  • Hao Zhang
  • Jihong Guan
  • Shuigeng ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9773)


Financial trend prediction is an interesting but also challenging research topic. In this paper, we exploit Twitter moods to boost next-day financial trend prediction performance based on deep network models. First, we summarize six-dimensional society moods from Twitter posts based on the profile of mood states Bipolar lexicon expanded by WordNet. Then, we combine Twitter moods and financial index by Deep Network models, and propose two methods. On the one hand, we utilize a Deep Neural Network of good fitting capability to evaluate and select predictive Twitter moods; On the other hand, we use a Convolutional Neural Network to explore temporal patterns of financial data and Twitter moods through convolution and pooling operations. Extensive experiments over real datasets are carried out to validate the performance of our methods. The results show that Twitter mood can improve prediction performance under the deep network models, and the Convolutional Neural Network based method performs best on most cases.


Financial trend prediction Twitter mood Deep neural network Convolutional neural network 



This work was partially supported by the Key Projects of Fundamental Research Program of Shanghai Municipal Commission of Science and Technology under grant No. 14JC1400300. Jihong Guan was partially supported by the Program of Shanghai Subject Chief Scientist.


  1. 1.
    Keras: Deep learning library for theano and tensorflow.
  2. 2.
    Theano is a python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
  3. 3.
    Twitter: number of monthly active users 2010–2015.
  4. 4.
    Bollen, J., Mao, H.: Twitter mood as a stock market predictor. Computer 44(10), 91–94 (2011)CrossRefGoogle Scholar
  5. 5.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  6. 6.
    Camerer, C.F., Loewenstein, G., Rabin, M.: Advances in Behavioral Economics. Princeton University Press, New Jersey (2011)Google Scholar
  7. 7.
    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 2014, Doha, Qatar, A meeting of SIGDAT, A Special Interest Group of the ACL, 25–29 October 2014, pp. 1415–1425 (2014)Google Scholar
  8. 8.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2327–2333. AAAI Press (2015)Google Scholar
  9. 9.
    Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)CrossRefGoogle Scholar
  10. 10.
    Giacomel, F., Pereira, A.C., Galante, R.: Improving financial time series prediction through output classification by a neural network ensemble. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9262, pp. 331–338. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  11. 11.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Huang, Y., Zhou, S., Huang, K., Guan, J.: Boosting financial trend prediction with twitter mood based on selective hidden Markov models. In: Renz, M., Shahabi, C., Zhou, X., Chemma, M.A. (eds.) DASFAA 2015. LNCS, vol. 9050, pp. 435–451. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  14. 14.
    Li, Q., Jiang, L., Li, P., Chen, H.: Tensor-based learning for predicting stock movements. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  15. 15.
    Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1023–1031 (2012)Google Scholar
  16. 16.
    Li, X., Wang, C., Dong, J., Wang, F., Deng, X., Zhu, S.: Improving stock market prediction by integrating both market news and stock prices. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 279–293. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Lin, Y., Guo, H., Hu, J.: An SVM-based approach for stock market trend prediction. In: The 2013 International Joint Conference on Neural Networks, pp. 1–7 (2013)Google Scholar
  18. 18.
    Mcnair, D., Lorr, M., Droppleman, C.: Profile of mood states. Educational & Industrial Testing Service, San Diego (1971)Google Scholar
  19. 19.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  20. 20.
    Ming, F., Wong, F., Liu, Z., Chiang, M.: Stock market prediction from WSJ: text mining via sparse matrix factorization. In: 2014 IEEE International Conference on Data Mining (ICDM), pp. 430–439. IEEE (2014)Google Scholar
  21. 21.
    Murphy, J.J.: Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance, New York (1999)Google Scholar
  22. 22.
    Pidan, D.: Selective Prediction with Hidden Markov Models. Master’s thesis, Technion (2013)Google Scholar
  23. 23.
    Pidan, D., El-Yaniv, R.: Selective prediction of financial trends with hidden Markov models. In: Advances in Neural Information Processing Systems, pp. 855–863 (2011)Google Scholar
  24. 24.
    Schumaker, R.P., Chen, H.: A discrete stock price prediction engine based on financial news. Computer 43(1), 51–56 (2010)CrossRefGoogle Scholar
  25. 25.
    Shi, S., Weigend, A.S.: Taking time seriously: Hidden markov experts applied to financial engineering. In: Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), pp. 244–252. IEEE (1997)Google Scholar
  26. 26.
    Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based twitter sentiment for stock prediction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 24–29 (2013)Google Scholar
  27. 27.
    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 2014, Doha, Qatar, A meeting of SIGDAT, A Special Interest Group of the ACL, 25–29 October 2014, pp. 1139–1145 (2014)Google Scholar
  28. 28.
    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
  29. 29.
    Sun, X.Q., Shen, H.W., Cheng, X.Q.: Trading network predicts stock price. Sci. Rep. 4(3711), 1–6 (2014)Google Scholar
  30. 30.
    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
  31. 31.
    Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 177–186 (2011)Google Scholar
  32. 32.
    Zhang, Y.: Prediction of financial time series with Hidden Markov Models. Master’s thesis, Simon Fraser University (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yifu Huang
    • 1
  • Kai Huang
    • 1
  • Yang Wang
    • 2
  • Hao Zhang
    • 1
  • Jihong Guan
    • 3
  • Shuigeng Zhou
    • 1
    Email author
  1. 1.School of Computer Science, and Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  2. 2.School of SoftwareJiangxi Normal UniversityNanchangChina
  3. 3.Department of Computer Science and TechnologyTongji UniversityShanghaiChina

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