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Noise, Information, and the Favorite-Longshot Bias in Parimutuel Predictions

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  • Marco Ottaviani
  • Peter Norman Sorensen

Abstract

According to the favorite-longshot bias, the expected return on an outcome tends to increase in the fraction of bets laid on that outcome. We derive testable implications for the direction and extent of the bias depending on the ratio of private information to noise present in the market. We link this ratio to observables such as the number of bettors, the number of outcomes, the amount of private information, the level of participation generated by recreational interest in the event, the divisibility of bets, the presence of ex post noise, as well as ex ante asymmetries across outcomes. (JEL D81, D83)

Suggested Citation

  • Marco Ottaviani & Peter Norman Sorensen, 2010. "Noise, Information, and the Favorite-Longshot Bias in Parimutuel Predictions," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 58-85, February.
    • Handle: RePEc:aea:aejmic:v:2:y:2010:i:1:p:58-85
    Note: DOI: 10.1257/mic.2.1.58
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    References listed on IDEAS

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    Cited by:

    1. Jinook Jeong & Jee Young Kim & Yoon Jae Ro, 2019. "On the efficiency of racetrack betting market: a new test for the favourite-longshot bias," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5817-5828, November.
    2. 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.
    3. Kajii, Atsushi & Watanabe, Takahiro, 2017. "Favorite–longshot bias in pari-mutuel betting: An evolutionary explanation," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 56-69.
    4. Leighton Vaughan Williams & Ming‐Chien Sung & Peter A. F. Fraser‐Mackenzie & John Peirson & Johnnie E. V. Johnson, 2018. "Towards an Understanding of the Origins of the Favourite–Longshot Bias: Evidence from Online Poker Markets, a Real‐money Natural Laboratory," Economica, London School of Economics and Political Science, vol. 85(338), pages 360-382, April.
    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. McAlvanah, Patrick & Moul, Charles C., 2013. "The house doesn’t always win: Evidence of anchoring among Australian bookies," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 87-99.
    7. Yu, Dian & Gao, Jianjun & Wang, Tongyao, 2022. "Betting market equilibrium with heterogeneous beliefs: A prospect theory-based model," European Journal of Operational Research, Elsevier, vol. 298(1), pages 137-151.
    8. 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.
    9. Koessler, Frédéric & Noussair, Charles & Ziegelmeyer, Anthony, 2012. "Information aggregation and belief elicitation in experimental parimutuel betting markets," Journal of Economic Behavior & Organization, Elsevier, vol. 83(2), pages 195-208.
    10. Karl Whelan, 2024. "Risk aversion and favourite–longshot bias in a competitive fixed‐odds betting market," Economica, London School of Economics and Political Science, vol. 91(361), pages 188-209, January.
    11. David Court & Benjamin Gillen & Jordi McKenzie & Charles R. Plott, 2018. "Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(1), pages 25-54, January.
    12. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    13. 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.
    14. Charles Moul & Joseph Keller, 2014. "Time to Unbridle U.S. Thoroughbred Racetracks? Lessons from Australian Bookies," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(3), pages 211-239, May.
    15. Lambert, Nicolas S. & Langford, John & Wortman Vaughan, Jennifer & Chen, Yiling & Reeves, Daniel M. & Shoham, Yoav & Pennock, David M., 2015. "An axiomatic characterization of wagering mechanisms," Journal of Economic Theory, Elsevier, vol. 156(C), pages 389-416.
    16. Zhang, Chi & Thijssen, Jacco, 2022. "On sticky bookmaking as a learning device in horse-racing betting markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    17. Matti Koivuranta & Marko Korhonen, 2019. "Misperception explains favorite-longshot bias: evidence from the Finnish and Swedish harness horse race markets," Empirical Economics, Springer, vol. 57(6), pages 2149-2160, December.
    18. Feess, Eberhard & Müller, Helge & Schumacher, Christoph, 2016. "Estimating risk preferences of bettors with different bet sizes," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1102-1112.
    19. Ulrik W. Nash, 2018. "Fair Odds for Noisy Probabilities," Papers 1811.12516, arXiv.org.
    20. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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