Modelling Movement of Stock Market Indexes with Data from Emoticons of Twitter Users

  • Alexander PorshnevEmail author
  • Ilya Redkin
  • Nikolay Karpov
Part of the Communications in Computer and Information Science book series (CCIS, volume 505)


The issue of using Twitter data to increase the prediction rate of stock price movements draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ emoticons to improve accuracy of predictions for DJIA and S&P500 stock market indices. We analyzed 1.6 billion tweets downloaded from February 13, 2013 to May 19, 2014. As a forecasting technique, we tested the Support Vector Machine (SVM), Neural Networks and Random Forest, which are commonly used for prediction tasks in finance analytics. The results of applying machine learning techniques to stock market price prediction are discussed.


Prediction Emoticons DJIA S&P500 Twitter Mood Support vectors machine Neural networks Random forest Behavioral finance 


  1. 1.
    Johnson, E.J., Tversky, A.: Affect, generalization, and the perception of risk. J. Pers. Soc. Psychol. 45, 20 (1983)CrossRefGoogle Scholar
  2. 2.
    Isen, A.M., Patrick, R.: The effect of positive feelings on risk taking: When the chips are down. Organ. Behav. Hum. Perform. 31, 194–202 (1983)CrossRefGoogle Scholar
  3. 3.
    Mayer, J.D., Gaschke, Y.N., Braverman, D.L., Evans, T.W.: Mood-congruent judgment is a general effect. J. Pers. Soc. Psychol. 63, 119 (1992)CrossRefGoogle Scholar
  4. 4.
    Schwarz, N., Clore, G.L.: Mood, misattribution, and judgments of well-being: informative and directive functions of affective states. J. Pers. Soc. Psychol. 45, 513 (1983)CrossRefGoogle Scholar
  5. 5.
    Isen, A.M., Means, B.: The influence of positive affect on decision-making strategy. Soc. Cogn. 2, 18–31 (1983)CrossRefGoogle Scholar
  6. 6.
    Nofsinger, J.R.: Social mood and financial economics. J. Behav. Fin. 6, 144–160 (2005)CrossRefGoogle Scholar
  7. 7.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100, 992–1026 (1992)CrossRefGoogle Scholar
  8. 8.
    Salganik, M.J., Dodds, P.S., Watts, D.J.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006)CrossRefGoogle Scholar
  9. 9.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: Learning from the behavior of others: conformity, fads, and informational cascades. J. Econ. Perspect. 12, 151–170 (1998)CrossRefGoogle Scholar
  10. 10.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)CrossRefGoogle Scholar
  11. 11.
    Selyukh, A.: Hackers send fake market-moving AP tweet on White House explosions | Reuters.
  12. 12.
  13. 13.
    Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. Eur. Fin. Manag. 20, 926–957 (2013)CrossRefGoogle Scholar
  14. 14.
    Ding, T., Fang, V., Zuo, D.: Stock market prediction based on time series data and market sentiment (2013).
  15. 15.
    Porshnev, A., Redkin, I., Shevchenko, A.: Improving Prediction of Stock Market Indices by Analyzing the Psychological States of Twitter Users. Social Science Research Network, Rochester (2013)CrossRefGoogle Scholar
  16. 16.
    Boia, M., Faltings, B., Musat, C.-C., Pu, P.: A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets, pp. 345–350. IEEE.
  17. 17.
    Rüping, S.: SVM kernels for time series analysis. Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund (2001)Google Scholar
  18. 18.
    Schnoebelen, T.: Do you smile with your nose? Stylistic variation in Twitter emoticons. University of Pennsylvania Working Papers in Linguistics, vol. 18, p. 14 (2012)Google Scholar
  19. 19.
    Vu, T.-T., Chang, S., Ha, Q. T., Collier, N.: An experiment in integrating sentiment features for tech stock prediction in Twitter. In: Proceedings of the Workshop on Information Extraction and Entity Analytics on Social Media Data (pp. 23–38). Mumbai, India: The COLING 2012 Organizing Committee (2012).
  20. 20.
    Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans. Inf. Syst. 27, 1–19 (2009)CrossRefGoogle Scholar
  21. 21.
    Mahajan, A., Dey, L., Haque, S.M.: Mining financial news for major events and their impacts on the market. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT 2008, pp. 423–426. IEEE (2008)Google Scholar
  22. 22.
    Groth, S.S., Muntermann, J.: An intraday market risk management approach based on textual analysis. Decis. Support Syst. 50, 680–691 (2011)CrossRefGoogle Scholar

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Authors and Affiliations

  • Alexander Porshnev
    • 1
    Email author
  • Ilya Redkin
    • 1
  • Nikolay Karpov
    • 1
  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussia

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