Abstract
Financial trend prediction has been a hot topic in both academia and industry. This paper proposes to exploit Twitter mood to boost financial trend prediction based on selective hidden Markov models (sHMM). First, we expand the profile of mood states (POMS) Bipolar lexicon to extract rich society moods from massive tweets. Then, we determine which mood has the most predictive power on the financial index based on Granger causality analysis (GCA). Finally, we extend sHMM to combine financial index and the selected Twitter mood to predict next-day trend. Extensive experiments show that our method not only outperforms the state-of-the-art methods, but also provides controllability to financial trend prediction.
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References
Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The annals of mathematical statistics 41(1), 164–171 (1970)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
Brand, M.: Coupled hidden markov models for modeling interacting processes. Tech. rep., MIT (1997)
Chow, C.K.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995). http://dx.doi.org/10.1007/BF00994018
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
El-Yaniv, R., Wiener, Y.: On the foundations of noise-free selective classification. The Journal of Machine Learning Research 11, 1605–1641 (2010)
Gilbert, E., Karahalios, K.: Widespread worry and the stock market. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, pp. 59–65 (2010)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society 37(3), 424–438 (1969)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)
Idvall, P., Jonsson, C.: Algorithmic trading: hidden markov models on foreign exchange data. Master’s thesis, Södertörn University (2008)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001). http://dl.acm.org/citation.cfm?id=645530.655813
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). http://doi.acm.org/10.1145/2339530.2339692
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)
Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, 2nd edn (2010)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012)
McNair, D.M., Lorr, M., Droppleman, L.F.: Profile of mood states. Educational and Industrial Testing Service (1971)
Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)
Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Tech. rep., Stanford University
Nofsinger, J.R.: Social mood and financial economics. The Journal of Behavioral Finance 6(3), 144–160 (2005)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008). http://dx.doi.org/10.1561/1500000011
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)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)
Schumaker, R.P., Chen, H.: A discrete stock price prediction engine based on financial news. Computer 43(1), 51–56 (2010)
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 (vol. 2: Short Papers), pp. 24–29 (2013)
Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. European Financial Management (2013). http://onlinelibrary.wiley.com/doi/10.1111/j.1468-036X.2013.12007.x/abstract
Stock, J.H., Watson, M.W.: Vector autoregressions. The Journal of Economic Perspectives 15(4), 101–115 (2001). http://www.jstor.org/stable/2696519
Sun, X.Q., Shen, H.W., Cheng, X.Q.: Trading network predicts stock price. Scientific Reports 4(3711), 1–6 (2014)
Trippi, R.R., Turban, E.: Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. McGraw-Hill, Inc (1992)
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Opinionfinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, pp. 34–35 (2005)
Wu, D., Ke, Y., Yu, J.X., Yu, P.S., Chen, L.: Detecting leaders from correlated time series. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5981, pp. 352–367. Springer, Heidelberg (2010)
Yang, B., Guo, C., Jensen, C.S.: Travel cost inference from sparse, spatio temporally correlated time series using markov models. Proc. VLDB Endow. 6(9), 769–780 (2013). http://dx.doi.org/10.14778/2536360.2536375
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)
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Huang, Y., Zhou, S., Huang, K., Guan, J. (2015). Boosting Financial Trend Prediction with Twitter Mood Based on Selective Hidden Markov Models. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9050. Springer, Cham. https://doi.org/10.1007/978-3-319-18123-3_26
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