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

Author

Listed:
  • Alasdair Brown

    (School of Economics, University of East Anglia)

  • Dooruj Rambaccussing

    (Economic Studies, University of Dundee)

  • James Reade

    (Department of Economics, University of Reading)

  • Giambattista Rossi

    (Department of Management, Birkbeck, University of London)

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 & James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair," Economics Discussion Papers em-dp2016-01, Department of Economics, University of Reading.
    • Handle: RePEc:rdg:emxxdp:em-dp2016-01
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    File URL: http://www.reading.ac.uk/web/FILES/economics/emdp2016118.pdf
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.

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    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

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