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Determinants of betting market efficiency

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  • Marshall Gramm
  • Douglas Owens

Abstract

Previous studies of efficient markets in parimutuel betting isolated only one race characteristic, determining efficiency by comparing subjective to objective probabilities of different groupings. By incorporating regression analysis and looking at a wide range of race specific variables, this study is able to isolate various factors which influence efficiency. Using a data set of 5020 races at 18 US racetracks, a standard favourite-longshot bias was found, which diminishes for races with larger pools and more horses in a field, and increases for races with higher quality fields and maiden races. When track-specific characteristics are factored out, similar results occur and it is also found that races on grass reduce the bias.

Suggested Citation

  • Marshall Gramm & Douglas Owens, 2005. "Determinants of betting market efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 12(3), pages 181-185.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:3:p:181-185
    DOI: 10.1080/1350485042000314352
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    References listed on IDEAS

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    1. Russell Sobel & S. Travis Raines, 2003. "An examination of the empirical derivatives of the favourite-longshot bias in racetrack betting," Applied Economics, Taylor & Francis Journals, vol. 35(4), pages 371-385.
    2. Asch, Peter & Malkiel, Burton G. & Quandt, Richard E., 1982. "Racetrack betting and informed behavior," Journal of Financial Economics, Elsevier, vol. 10(2), pages 187-194, July.
    3. Busche, Kelly & Hall, Christopher D, 1988. "An Exception to the Risk Preference Anomaly," The Journal of Business, University of Chicago Press, vol. 61(3), pages 337-346, July.
    4. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    5. David Paton & Leighton Vaughan Williams, 1998. "Do betting costs explain betting biases?," Applied Economics Letters, Taylor & Francis Journals, vol. 5(5), pages 333-335.
    6. Kelly Busche & W. David Walls, 2001. "Breakage and betting market efficiency: evidence from the horse track," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 601-604.
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    Cited by:

    1. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Costa Sperb, L.F. & Sung, M.-C. & Ma, T. & Johnson, J.E.V., 2022. "Turning the heat on financial decisions: Examining the role temperature plays in the incidence of bias in a time-limited financial market," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1142-1157.
    3. Chin-Tsai Lin & Chien-Hua Lai, 2006. "Substitute effects between Lotto and Big Lotto in Taiwan," Applied Economics Letters, Taylor & Francis Journals, vol. 13(10), pages 655-658.
    4. 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.
    5. Sperb, Luis Felipe Costa & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2019. "Keeping a weather eye on prediction markets: The influence of environmental conditions on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 35(1), pages 321-335.

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