Predicting Asset Value through Twitter Buzz

  • Xue Zhang
  • Hauke Fuehres
  • Peter A. Gloor
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 113)


This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar% t ”, “$% t ”, “gold% t ”, “oil% t , “job% t ” and “economy% t ” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$% t ”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.


Stock Market Granger Causality Earthquake Early Warning Currency Exchange Rate Twitter Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Antweiler, W., Frank, M.Z.: Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. Journal of Finance 59(3), 1259–1294 (2004)CrossRefGoogle Scholar
  2. 2.
    Asur, S., Huberman, B.A.: Predicting the Future With Social Media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2010)Google Scholar
  3. 3.
    Bollen, J., Mao, H., Zheng, X.J.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)CrossRefGoogle Scholar
  4. 4.
    Boyd, D., Golder, S., Lotan, G.: Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In: Proceeding of 43rd Hawaii International Conference on System Sciences, HICSS (2010)Google Scholar
  5. 5.
    Choudhury, M.D., Sundaram, H., John, A., Seligmann, D.D.: Can Blog Communication Dynamics be Correlated with Stock Market Activity? In: Proceedings of the 9th ACM Conference on Hypertext and Hypermedia (2010)Google Scholar
  6. 6.
    Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191–1194 (2002)CrossRefGoogle Scholar
  7. 7.
    Gayo-Avello, D., Metaxas, P.T., Mustafaraj, E.: On the Unpredictability of Elections using Social Media Data. In: Interdisciplinary Workshop on Information and Decision in Social Networks. MIT (2011)Google Scholar
  8. 8.
    Gilbert, E., Karahalios, K.: Widespread Worry and the Stock Market. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, ICWSM (2010)Google Scholar
  9. 9.
    Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. First Monday 14(1) (2009)Google Scholar
  10. 10.
    Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: Understanding Microblogging Usage and Communities. In: Proceeding of 9th WebKDD and 1st SNA-KDD Workshop on Web Mining and Social Network Analysis (2007)Google Scholar
  11. 11.
    Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the Social Web. In: IAPR 2nd Workshop on Cognitive Information Processing (2010)Google Scholar
  12. 12.
    Lerner, J.S., Keltner, D.: Fear, Anger, and Risk. Journal of Personality and Social Psychology 81(1), 146–159 (2001)CrossRefGoogle Scholar
  13. 13.
    Lerner, J.S., Small, D.A., Loewenstein, G.F.: Heart strings and Purse Strings: Carryover Effects of Emotions on Economic Decisions. Psychological Science 15, 337–341 (2004)CrossRefGoogle Scholar
  14. 14.
    Loewenstein, G.F., Weber, E.U., Hsee, C.K., Welch, N.: Risk as Feeling. Psychological Bulletin 127(2), 267–286 (2001)CrossRefGoogle Scholar
  15. 15.
    Shiv, B., Loewenstein, G.F., Bechara, A., Damasio, H., Damasio, A.R.: Investment Behavior and the Negative Side of Emotion. Psychological Science 16, 435–439 (2005)Google Scholar
  16. 16.
    Sprenger, T.O., Welpe, I.M.: Tweets and Trades – The Information Content of Stock Microblogs (2010), SSRN:
  17. 17.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, ICWSM (2010)Google Scholar
  18. 18.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says What to Whom on Twitter. In: Proceedings of the 20th International Conference on World Wide Web (2011)Google Scholar
  19. 19.
    Zhang, X., Fuehres, H., Gloor, P.: Predicting Stock Market Indicators Through Twitter: I hope it is not as bad as I fear (2010),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xue Zhang
    • 1
    • 2
  • Hauke Fuehres
    • 2
  • Peter A. Gloor
    • 2
  1. 1.Department of Mathematic and Systems ScienceNational University of Defense TechnologyChangshaP.R.China
  2. 2.MIT Center for Collective IntelligenceCambridgeUSA

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