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Learning and Efficiency in a Gambling Market

Author

Listed:
  • James D. Dana

    (Kellogg Graduate School of Management, Northwestern University, Evanston, Illinois 60208)

  • Michael M. Knetter

    (Department of Economics, Dartmouth College, Hanover, New Hampshire 03755, and NBER)

Abstract

We present a statistical model which uses data on National Football League games and betting lines to study how agents learn from past outcomes and to test market efficiency. Using Kalman Filter estimation, we show that terms' abilities exhibit substantial week-to-week variation during the season. This provides an ideal environment in which to study how agents learn from past information. While we do not find strong evidence of market inefficiency, we are able to make several observations on market learning. In particular, agents have more difficulty learning from "noisy" observations and appear to weight recent observations less that our statistical model suggests is optimal.

Suggested Citation

  • James D. Dana & Michael M. Knetter, 1994. "Learning and Efficiency in a Gambling Market," Management Science, INFORMS, vol. 40(10), pages 1317-1328, October.
  • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:10:p:1317-1328
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    File URL: http://dx.doi.org/10.1287/mnsc.40.10.1317
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    Citations

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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, Research Program on Forecasting.
    2. Vaughan Williams, Leighton & Stekler, Herman O., 2010. "Sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 445-447, July.
      • Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, Research Program on Forecasting, revised Jan 2007.
    3. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    4. Adi Schnytzer & Guy Weinberg, 2011. "Testing for Home Team and Favorite Biases in the Australian Rules Football Fixed Odds and Point Spread Betting Markets," Working Papers 2011-13, Bar-Ilan University, Department of Economics.
    5. Miller, Thomas W. & Rapach, David E., 2013. "An intra-week efficiency analysis of bookie-quoted NFL betting lines in NYC," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 10-23.
    6. Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Bar-Ilan University, Department of Economics.

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