European Journal of Business Science and Technology 2015, 1(1):24-35 | DOI: 10.11118/ejobsat.v1i1.35

Impact of Social Media on the Stock Market: Evidence from Tweets

Vojtěch Fiala1, Svatopluk Kapounek1, Ondřej Veselý1
1 Mendel University in Brno, Czech Republic

The paper deals with the impact of the economic agent sentiment on the return for Apple and Microsoft stocks. We employed text mining procedures to analyze Twitter messages with either negative or positive sentiment towards the chosen stock titles. Those sentiments were identified by developed algorithms which are capable of identifying sentiment towards companies and also counting the numbers of tweets in the same group. This resulted in counts of tweets with positive and negative sentiment. Then we ran analysis in order to find causality between sentiment levels and the stock price of companies. To identify causal effects we applied Granger causality tests. We found bilateral causality between the risk premium and the amount of news distributed by Twitter messages.

Keywords: stock returns, Granger causality, text mining, sentiment analysis, CAPM
JEL classification: C1, C12, G12, G17

Published: November 11, 2015  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Fiala, V., Kapounek, S., & Veselý, O. (2015). Impact of Social Media on the Stock Market: Evidence from Tweets. European Journal of Business Science and Technology, 1(1), 24-35. doi: 10.11118/ejobsat.v1i1.35
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Aitchison, J. and Silvey, S. D. 1958. Maximum Likelihood Estimation of Parameters Subject to Restraints. Annals of Mathematical Statistics, 29, 813-828. Go to original source...
    2. Antweiler, W. and Frank, M. Z. 2004. Is all that talk just noise? The information content of Internet stock message boards. Journal of Finance, 59, 1259-1294. Go to original source...
    3. Arias, M., Arratia, A. and Xuriguera, R. 2013. Forecasting with Twitter Data. ACM Transactions on Intelligent Systems and Technology, 5 (1). Go to original source...
    4. Berndt, E. R., Savin, N. E. 1977. Conflict Among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model. Econometrica, 45 (5), 1263-1278. Go to original source...
    5. Bollen, J., Mao, H. and Zeng, X. J. 2011. Twitter mood predicts the stock market. Journal of Computational Science, 1-8. Go to original source...
    6. Chung, S. and Liu, S. 2011. Predicting Stock Market Fluctuations from Twitter: An analysis of the predictive powers of realtime social media. [online]. Available at: http://www.stat.berkeley.edu/~aldous/157/Old_Projects/Sang_Chung_Sandy_Liu.pdf. [Accessed 2015, February 16].
    7. Dolan, R. J. 2002. Emotion, cognition, and behavior. Science, 298, 1191-1194. Go to original source...
    8. Friedman, M. 1953. The case for flexible exchange rates. Essays in Positive Economics. University of Chicago Press, Chicago.
    9. Fama, E. F. 1965. The Behavior of Stock-Market Prices. The Journal of Business, 38 (1), 34-105. Go to original source...
    10. Fama, E. F. 1970. Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25 (2), 383-417. Go to original source...
    11. Fama, E. F. 1991. Efficient capital markets II. Journal of Finance, 46 (5), 1575-1617. Go to original source...
    12. Gilbert, E. and Karahalios, K. 2010. Widespread Worry and the Stock Market. In Proceedings of the international conference on weblogs and social media (ICWSM 10). Go to original source...
    13. Granger, C. W. J. 1969. Investigating causal relations by econometric models and cross-spectral models. Econometrica, 37, 424-438. Go to original source...
    14. Kim, S. H. and Kim, D. K. 2014. Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior & Organization, 107, 708-729. Go to original source...
    15. Kuleshov, V. 2011. Can Twitter predict the stock market? [online]. Available at: http://cs229.stanford.edu/proj2011/Kuleshov-CanTwitterPredictTheStockMarket.pdf. [Accessed 2015, February 16].
    16. LeRoy, S. and Porter, R. 1981. The present-value relation: tests based on implied variance bounds. Econometrica, 49, 97-113. Go to original source...
    17. Ross, S. A., 1976. The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13, 341-360. Go to original source...
    18. Shiller, J. R. 1981. Do stock prices move too much to be justified by subsequent changes in dividends? American Economic Review, 71, 421-436. Go to original source...
    19. Shiller, J. R. 2003. From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17 (1), 83-104. Go to original source...
    20. Silvey, S. D. 1959. The Lagrange Multiplier Test. Annals of Mathematical Statistics, 30, 389-407. Go to original source...
    21. Sims, C. 1972. Money, income, and causality. American Economic Review, 62, 540-552.
    22. Wald, A. 1943. Tests of Hypotheses Concerning Several Parameters When the Number of Observations is Large. Transactions of the American Mathematical Society, 54, 426-482. Go to original source...
    23. Zhang, X., Fuehres, H. and Gloor, P. 2011. Predicting Stock Market Indicators Through Twitter: "I hope it is not as bad as I fear". Procedia - Social and Behavioral Sciences, 26, 55-62. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content