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Testing the Efficiency of Markets in the 2002 World Cup

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  • Ricard Gil
  • Steven D. Levitt

Abstract

Trading data from the gambling market for the 2002 World Cup provide a unique window through which to test theories of market efficiency. This market provides many of the benefits of a laboratory experiment, but with much higher stakes, experienced participants, and a naturally-occurring environment. The primary drawback of the data is the relatively small number of trades. The evidence concerning market efficiency is mixed. Although markets respond strongly to goals being scored, there is some evidence that prices continue to trend higher for 10-15 minutes after a goal. We also observe systematically negative returns for bets on the pre-game favorite, consistent with the biases seen in wagering on other sports. We document the endogenous emergence of market makers. These market makers are involved in a large share of trades. Increasing from two active market makers to five or more market makers does not appear to improve the functioning of the market. On average, the market makers earn slightly negative returns, implying that other traders are able to identify situations in which market makers are setting inefficient prices.

Suggested Citation

  • Ricard Gil & Steven D. Levitt, 2007. "Testing the Efficiency of Markets in the 2002 World Cup," Journal of Prediction Markets, University of Buckingham Press, vol. 1(3), pages 255-270, December.
    • Handle: RePEc:buc:jpredm:v:1:y:2007:i:3:p:255-270
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    Cited by:

    1. Dagaev, Dmitry & Stoyan, Egor, 2020. "Parimutuel betting on the eSports duels: Evidence of the reverse favourite-longshot bias," Journal of Economic Psychology, Elsevier, vol. 81(C).
    2. Michael Sinkey & Trevon Logan, 2014. "Does the Hot Hand Drive the Market? Evidence from College Football Betting Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 40(4), pages 583-603, September.
    3. Choi, Darwin & Hui, Sam K., 2014. "The role of surprise: Understanding overreaction and underreaction to unanticipated events using in-play soccer betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 614-629.
    4. Frantisek Kopriva, 2015. "Constant Bet Size? Don't Bet on It! Testing Expected Utility Theory on Betfair Data," CERGE-EI Working Papers wp545, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Marco Ottaviani & Peter Norman Sørensen, 2015. "Price Reaction to Information with Heterogeneous Beliefs and Wealth Effects: Underreaction, Momentum, and Reversal," American Economic Review, American Economic Association, vol. 105(1), pages 1-34, January.
    6. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    7. Dominic Cortis, 2015. "Expected Values And Variances In Bookmaker Payouts: A Theoretical Approach Towards Setting Limits On Odds," Journal of Prediction Markets, University of Buckingham Press, vol. 9(1), pages 1-14.
    8. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.
    9. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. 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.
    11. Marco Ottaviani & Peter Norman Sørensen, 2009. "Aggregation of Information and Beliefs: Asset Pricing Lessons from Prediction Markets," Discussion Papers 09-14, University of Copenhagen. Department of Economics.
    12. Forsell, Eskil & Viganola, Domenico & Pfeiffer, Thomas & Almenberg, Johan & Wilson, Brad & Chen, Yiling & Nosek, Brian A. & Johannesson, Magnus & Dreber, Anna, 2019. "Predicting replication outcomes in the Many Labs 2 study," Journal of Economic Psychology, Elsevier, vol. 75(PA).
    13. Kai Fischer & Justus Haucap, 2022. "Home advantage in professional soccer and betting market efficiency: The role of spectator crowds," Kyklos, Wiley Blackwell, vol. 75(2), pages 294-316, May.
    14. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    15. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    16. Easton, Stephen & Uylangco, Katherine, 2010. "Forecasting outcomes in tennis matches using within-match betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 564-575, July.

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