IDEAS home Printed from https://ideas.repec.org/p/dun/dpaper/293.html
   My bibliography  Save this paper

Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair

Author

Listed:
  • Alasdair Brown
  • Dooruj Rambaccussing
  • J. James Reade
  • Giambattista Rossi

Abstract

Information extracted from social media has been used by academics, and increasingly by practitioners, to predict stock returns. But to what extent does social media output predict asset fundamentals, and not simply short-term returns? In this paper we analyse 13.8m posts on Twitter, and high-frequency betting data from Betfair, concerning English Premier League soccer matches in 2013/14. Crucially, asset fundamentals are revealed at the end of play. We find that the Tweets of certain journalists, and the tone of all Tweets, contain fundamental information not revealed in betting prices. In particular, Tweets aid in the interpretation of news during matches.

Suggested Citation

  • Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair," Dundee Discussion Papers in Economics 293, Economic Studies, University of Dundee.
    • Handle: RePEc:dun:dpaper:293
    as

    Download full text from publisher

    File URL: http://www.dundee.ac.uk/media/dundeewebsite/economicstudies/documents/discussion/DDPE_293.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Erik Snowberg & Justin Wolfers, 2010. "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?," Journal of Political Economy, University of Chicago Press, vol. 118(4), pages 723-746, August.
    2. Alasdair Brown & Fuyu Yang, 2017. "The Role of Speculative Trade in Market Efficiency: Evidence from a Betting Exchange," Review of Finance, European Finance Association, vol. 21(2), pages 583-603.
    3. Leighton Vaughan Williams & J. James Reade, 2016. "Forecasting Elections," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 308-328, July.
    4. Christopher N. Avery & Judith A. Chevalier & Richard J. Zeckhauser, 2016. "The "CAPS" Prediction System and Stock Market Returns," Review of Finance, European Finance Association, vol. 20(4), pages 1363-1381.
    5. Karen Croxson & J. James Reade, 2014. "Information and Efficiency: Goal Arrival in Soccer Betting," Economic Journal, Royal Economic Society, vol. 124(575), pages 62-91, March.
    6. Paul C. Tetlock, 2011. "All the News That's Fit to Reprint: Do Investors React to Stale Information?," The Review of Financial Studies, Society for Financial Studies, vol. 24(5), pages 1481-1512.
    7. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    8. Williams, Leighton Vaughan & Paton, David, 1997. "Why Is There a Favourite-Longshot Bias in British Racetrack Betting Markets?," Economic Journal, Royal Economic Society, vol. 107(440), pages 150-158, January.
    9. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    10. Alex Edmans & Diego García & Øyvind Norli, 2007. "Sports Sentiment and Stock Returns," Journal of Finance, American Finance Association, vol. 62(4), pages 1967-1998, August.
    11. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    12. Ali, Mukhtar M, 1977. "Probability and Utility Estimates for Racetrack Bettors," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 803-815, August.
    13. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    14. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    15. Snyder, Wayne W, 1978. "Horse Racing: Testing the Efficient Markets Model," Journal of Finance, American Finance Association, vol. 33(4), pages 1109-1118, September.
    16. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    17. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    18. Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
    19. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    20. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    21. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    2. Peeters, Thomas, 2018. "Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results," International Journal of Forecasting, Elsevier, vol. 34(1), pages 17-29.
    3. Alasdair Brown & James Reade & Leighton Vaughan Williams, 2018. "Prediction Markets and Poll Releases: When Are Prices Most Informative?," Economics Discussion Papers em-dp2018-02, Department of Economics, University of Reading.
    4. Maribel Serna Rodríguez & Andrés Ramírez Hassan & Alexander Coad, 2019. "Uncovering Value Drivers of High Performance Soccer Players," Journal of Sports Economics, , vol. 20(6), pages 819-849, August.
    5. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    6. Dmitry Dagaev & Egor Stoyan, 2019. "Parimutuel Betting On The Esports Duels: Reverse Favourite-Longshot Bias And Its Determinants," HSE Working papers WP BRP 216/EC/2019, National Research University Higher School of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Ine!ciencies: Evidence from Twitter and Betfair," Working Papers 2016-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2018. "Forecasting With Social Media: Evidence From Tweets On Soccer Matches," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1748-1763, July.
    3. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    4. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    5. Alasdair Brown & Fuyu Yang, 2015. "Adverse Selection, Speed Bumps and Asset Market Quality," University of East Anglia Applied and Financial Economics Working Paper Series 070, School of Economics, University of East Anglia, Norwich, UK..
    6. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    7. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    8. Breitmayer, Bastian & Massari, Filippo & Pelster, Matthias, 2019. "Swarm intelligence? Stock opinions of the crowd and stock returns," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 443-464.
    9. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    10. Eierle, Brigitte & Klamer, Sebastian & Muck, Matthias, 2022. "Does it really pay off for investors to consider information from social media?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    11. Manuel Ammann & Nic Schaub, 2021. "Do Individual Investors Trade on Investment-Related Internet Postings?," Management Science, INFORMS, vol. 67(9), pages 5679-5702, September.
    12. Goto, Shingo & Yamada, Toru, 2023. "What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 252-270.
    13. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    14. Oliver Merz & Raphael Flepp & Egon Franck, 2019. "Does sentiment harm market efficiency? An empirical analysis using a betting exchange setting," Working Papers 381, University of Zurich, Department of Business Administration (IBW).
    15. Merz, Oliver & Flepp, Raphael & Franck, Egon, 2021. "Sonic Thunder vs. Brian the Snail: Are people affected by uninformative racehorse names?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    16. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    17. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    18. André Betzer & Jan Philipp Harries, 2022. "How online discussion board activity affects stock trading: the case of GameStop," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(4), pages 443-472, December.
    19. Martin Kukuk & Stefan Winter, 2008. "An Alternative Explanation of the Favorite-Longshot Bias," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 2(2), pages 79-96, September.
    20. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).

    More about this item

    Keywords

    social media; prediction markets; fundamentals; sentiment; mispricing;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dun:dpaper:293. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Andrzej Kwiatkowski (email available below). General contact details of provider: https://edirc.repec.org/data/dedunuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.