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Using Social Media to Identify Market Ine!ciencies: Evidence from Twitter and Betfair

Author

Listed:
  • Alasdair Brown

    () (University of East Anglia)

  • Dooruj Rambaccussing

    () (University of Dundee)

  • J. James Reade

    () (University of Reading)

  • Giambattista Rossi

    () (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 & 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, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2016-002
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    File URL: https://www2.gwu.edu/~forcpgm/2016-002.pdf
    File Function: First version, 2016
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    References listed on IDEAS

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    1. repec:eee:intfor:v:34:y:2018:i:1:p:17-29 is not listed on IDEAS

    More about this item

    Keywords

    social media; prediction markets; fundamentals; sentiment; mispricing;

    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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