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Learning and Efficiency in a Gambling Market

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Author Info

  • 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.

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File URL: http://dx.doi.org/10.1287/mnsc.40.10.1317
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 40 (1994)
Issue (Month): 10 (October)
Pages: 1317-1328

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Handle: RePEc:inm:ormnsc:v:40:y:1994:i:10:p:1317-1328

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Related research

Keywords: market efficiency; optimal learning; Kalman filter;

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Citations

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Cited by:
  1. Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, Research Program on Forecasting, revised Jan 2007.
  2. Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Department of Economics, Bar-Ilan University.
  3. 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, Department of Economics, Bar-Ilan University.
  4. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
  5. 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.
  6. 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.

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