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Sentiment Bias and Asset Prices: Evidence from Sports Betting Markets and Social Media

Author

Listed:
  • Arne Feddersen

    (University of Southern Denmark, Department of Environmental and Business Economics)

  • Brad Humphreys

    (West Virginia University, College of Business and Economics)

  • Brian Soebbing

    (Louisiana State University, School of Kinesiology)

Abstract

Previous research using attendance-based proxies for the number of investors with sentiment bias supported the presence of investor sentiment in sports betting markets. We use data from social media (Facebook \Likes") to proxy for participants with investor sentiment and analyze variation in prices set by bookmakers for evidence of influence from investor sentiment. Based on prices and outcomes over two seasons in seven professional sports leagues in Europe and North America, we develop new evidence that asset prices reflect the presence of investor sentiment in these markets, or that bookmakers believe they exist, by offering favorable betting lines for popular teams. Favorable prices do not translate into a higher probability of winning a bet.

Suggested Citation

  • Arne Feddersen & Brad Humphreys & Brian Soebbing, 2013. "Sentiment Bias and Asset Prices: Evidence from Sports Betting Markets and Social Media," Working Papers 13-07, Department of Economics, West Virginia University.
    • Handle: RePEc:wvu:wpaper:13-07
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    File URL: http://be.wvu.edu/phd_economics/pdf/13-07.pdf
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    References listed on IDEAS

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    1. Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2018. "Sentiment Bias in National Basketball Association Betting," Journal of Sports Economics, , vol. 19(4), pages 455-472, May.
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    Cited by:

    1. 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).
    2. Lenten, Liam J.A. & Crosby, Paul & McKenzie, Jordi, 2019. "Sentiment and bias in performance evaluation by impartial arbitrators," Economic Modelling, Elsevier, vol. 76(C), pages 128-134.
    3. Barge-Gil, Andrés & García-Hiernaux, Alfredo, 2019. "Staking plans in sports betting under unknown true probabilities of the event," MPRA Paper 92196, University Library of Munich, Germany.
    4. Mills, Brian M. & Salaga, Steven, 2018. "A natural experiment for efficient markets: Information quality and influential agents," Journal of Financial Markets, Elsevier, vol. 40(C), pages 23-39.
    5. Andre Boik, 2017. "The empirical effects of competition on third‐degree price discrimination in the presence of arbitrage," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(4), pages 1023-1036, November.
    6. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.
    7. Andrés Barge-Gil & Alfredo Garcia-Hiernaux, 2020. "Staking in Sports Betting Under Unknown Probabilities: Practical Guide for Profitable Bettors," Journal of Sports Economics, , vol. 21(6), pages 593-609, August.
    8. Christian Deutscher & David Winkelmann & Marius Otting, 2020. "Bookmakers' mispricing of the disappeared home advantage in the German Bundesliga after the COVID-19 break," Papers 2008.05417, arXiv.org, revised Aug 2020.

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    More about this item

    Keywords

    investor sentiment; sports betting; social media;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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