Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Keras: Deep learning library for theano and tensorflow. http://keras.io/
Theano is a python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. http://www.deeplearning.net/software/theano/
Twitter: number of monthly active users 2010–2015. http://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/
Bollen, J., Mao, H.: Twitter mood as a stock market predictor. Computer 44(10), 91–94 (2011)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Camerer, C.F., Loewenstein, G., Rabin, M.: Advances in Behavioral Economics. Princeton University Press, New Jersey (2011)
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)
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)
Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)
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)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, Q., Jiang, L., Li, P., Chen, H.: Tensor-based learning for predicting stock movements. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
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)
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)
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)
Mcnair, D., Lorr, M., Droppleman, C.: Profile of mood states. Educational & Industrial Testing Service, San Diego (1971)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
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)
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)
Pidan, D.: Selective Prediction with Hidden Markov Models. Master’s thesis, Technion (2013)
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)
Schumaker, R.P., Chen, H.: A discrete stock price prediction engine based on financial news. Computer 43(1), 51–56 (2010)
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)
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)
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)
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)
Sun, X.Q., Shen, H.W., Cheng, X.Q.: Trading network predicts stock price. Sci. Rep. 4(3711), 1–6 (2014)
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)
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)
Zhang, Y.: Prediction of financial time series with Hidden Markov Models. Master’s thesis, Simon Fraser University (2004)
Acknowledgement
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, Y., Huang, K., Wang, Y., Zhang, H., Guan, J., Zhou, S. (2016). Exploiting Twitter Moods to Boost Financial Trend Prediction Based on Deep Network Models. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-42297-8_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42296-1
Online ISBN: 978-3-319-42297-8
eBook Packages: Computer ScienceComputer Science (R0)