Skip to main content

Model and forecast stock market behavior integrating investor sentiment analysis and transaction data

Abstract

Social network media analytics is showing promise for prediction of financial markets. However, the true value of such data is unclear due to a lack of consensus on which instruments can be predicted. In this paper, we investigate whether measurements of collective emotional states derived from large scale network feeds are correlated to the stock transaction data over time. The information space corresponding to stocks is divided into the network public opinion space \(Opinion\_Space\) and the realistic transaction space \(Behavior\_Space\). We then handle the information and generate the multidimensional time series from them respectively. Furthermore, Granger causality analysis and information theory measures are used to find and demonstrate that social media sentiments contain statistically significant ex-ante information on the future prices. At last, we propose our separate-LSTM model and the experimental results of six stocks which are randomly selected indicate that financial data predictions can be significantly improved through our model by the fusion of network public opinion emotions and realistic transaction data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. 1.

    Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54(3), 241–251 (2011)

    Article  Google Scholar 

  2. 2.

    Chairi, I., Griol, D., Manuel, M.J.: Modeling human-machine interaction by means of a sample selection method. In: 13th International Conference on Practical Applications of Agents, Multi-Agent Systems, and Sustainability, vol. 524, pp. 191–200 (2015)

  3. 3.

    Tu, W., Cheung, D., Mamoulis, N.: Time-sensitive opinion mining for prediction. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

  4. 4.

    Wang, S., et al.: Burst time prediction in cascades. In: Twenty-Ninth AAAI Conference on Artificial Intelligence AAAI Press (2015)

  5. 5.

    Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., Rosenquist, J.N.: Understanding the demographics of twitter users. In: The Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, pp. 554–557. The AAAI Press, Menlo Park, CA (2011)

  6. 6.

    Zheludev, I., Smith, R., Aste, T.: When can social media lead financial markets? Sci. Rep. 4(7489), 4213–4213 (2015)

    Google Scholar 

  7. 7.

    de Vries, L., Gensler, S., Leeflang, P.S.H.: Popularity of brand posts on BrandFan pages: an investigation of the effects of social media marketing. J. Interact. Mark. 26, 83–91 (2012)

    Article  Google Scholar 

  8. 8.

    Asur, S., Huberman, B.A.: Predicting the future with social media. In: The 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Theory, Toronto, Canada. doi:10.1109/WIIAT.2010.63 (2010)

  9. 9.

    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: The Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, USA. The AAAI Press Menlo Park, CA (2010)

  10. 10.

    Bollena, J., Maoa, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  11. 11.

    Wang, C., Huberman, B.A.: How random are online social interactions? Sci. Rep. 2(9), 168–168 (2012)

    Google Scholar 

  12. 12.

    Li, X., Xie, H., Chen, L., Wang, J., Deng, Xiaotie: News impact on stock price return via sentiment analysis. Knowl. Based Syst. 69, 14–23 (2014)

    Article  Google Scholar 

  13. 13.

    Siganos, A., Vagenas-Nanos, E., Verwijmeren, P.P.: Facebook’s daily sentiment and international stock markets. J. Econ. Behav. Org. 107, 730–743 (2014)

    Article  Google Scholar 

  14. 14.

    Smailović, J., Grčar, M., Lavrač, N., Žnidaršič, M.: Stream-based active learning for sentiment analysis in the financial domain. Inform. Sci. 285(1), 181–203 (2014)

    Article  Google Scholar 

  15. 15.

    Kukacka, J., Barunik, J.: Behavioural breaks in the heterogeneous agent model: the impact of herding, overconfidence, and market sentiment. Phys. A 392(23), 5920–5938 (2013)

    MathSciNet  Article  Google Scholar 

  16. 16.

    Li, Q., Wang, T.J., Li, P., Liu, L., Gong, Q., Chen, Y.: The effect of news and public mood on stock movements. Inform. Sci. 278(10), 826–840 (2014)

    Article  Google Scholar 

  17. 17.

    Cabrera-Paniagua, D., Cubillos, C., Vicari, R., Urra, E.: Decision-making system for stock exchange market using artificial emotions. Expert Syst. Appl. 42(20), 7070–7083 (2015)

    Article  Google Scholar 

  18. 18.

    Jheng-Long, W., Liang-Chih, Y., Chang, P.-C.: An intelligent stock trading system using comprehensive features. Appl. Soft Comput. 23(5), 39–50 (2014)

    Google Scholar 

  19. 19.

    Saavedra, S., Duch, J., Uzzi, B.: Tracking traders’ understanding of the market using e-communication data. PLoS ONE 6(10), e26705 (2011)

    Article  Google Scholar 

  20. 20.

    Mao, Y., Wei, W., Wang, B., Liu, B.: Correlating S&P500 stocks with Twitter data. The First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, Beijing, China. ACM, New York (2012)

  21. 21.

    Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro blogging activity. In: The Fifth ACM International Conference on Web search and Data Mining, Seattle, USA. ACM, New York (2012)

  22. 22.

    Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using google trends. Scientific Reports 3, no. 1684 (2013)

  23. 23.

    Challet, D., Bel Hadj Ayed, A.: Predicting financial markets with Google Trends and not so random keywords. arXiv:1307.4643 (2013)

  24. 24.

    Preis, T., Reith, D., Stanley, H.E.: Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. T. R. Soc. A 368, 5707–5719 (2010)

    Article  MATH  Google Scholar 

  25. 25.

    Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M.: Web search queries can predict stock market volumes. PloS ONE 7, e40014 (2012)

    Article  Google Scholar 

  26. 26.

    Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter I hope it is not as bad as I fear. Proc. Soc. Behav. Sci. 26, 55–62 (2011)

    Article  Google Scholar 

  27. 27.

    Kallas, M., Honeine, P., Francis, C., Amoud, H.: Kernel autoregressive models using Yule–Walker equations. Signal Process. 93(11), 3053–3061 (2013)

    Article  MATH  Google Scholar 

  28. 28.

    Liu, C.-S.: A method of Lie-symmetry GL(n,image) for solving non-linear dynamical systems. Int. J. Non-Linear Mech. 52, 85–95 (2013)

    Article  Google Scholar 

  29. 29.

    Upadhyay, A., Bandyopadhyay, G., Dutta, A.: Forecasting stock performance in Indian market using multinomial logistic regression. J. Bus. Stud. Q. 3(3), 16–39 (2012)

    Google Scholar 

  30. 30.

    Sureshkumar, K.K., Elango, N.M.: An efficient approach to forecast Indian stock market price and their performance analysis. Int. J. Comput. Appl. 34, 44–49 (2011)

    Google Scholar 

  31. 31.

    Mehrara, M., Moeini, A., Ahrari, M., Ghafari, A.: Using technical analysis with neural network for forecasting stock price index in Tehran stock exchange. Middle East. Fin. Econ. 6(6), 50–61 (2010)

    Google Scholar 

  32. 32.

    Agrawal, S., Jindal, M., Pillai, G.N.: Momentum analysis based stock market prediction using adaptive Neuro-Fuzzy inference system(ANFIS). In: Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong vol. 1, March 17–19 (2010)

  33. 33.

    Brody, D., Meister, B., Parry, M.: Informational inefficiency in financial markets. Math. Fin. Econ 6, 249–259 (2012)

    MathSciNet  Article  MATH  Google Scholar 

Download references

Acknowledgements

This paper is supported by National Natural Science Foundation of China (Grant No: 40976108) and Science Foundation of The Chinese Education Commission China’s State Oceanic Administration

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lingyu Xu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, G., Xu, L. & Xue, Y. Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Cluster Comput 20, 789–803 (2017). https://doi.org/10.1007/s10586-017-0803-x

Download citation

Keywords

  • Stock transaction
  • Emotions
  • Time dependence
  • LSTM
  • Network public opinion
  • Granger causality
  • Predictive analysis