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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
    DOI: 10.1287/mnsc.40.10.1317
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    References listed on IDEAS

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    1. Roger C. Vergin & Michael Scriabin, 1978. "Winning Strategies for Wagering on National Football League Games," Management Science, INFORMS, vol. 24(8), pages 809-818, April.
    2. Russo, Benjamin & Gandar, John M. & Zuber, Richard A., 1989. "Market rationality tests based on cross-equation restrictions," Journal of Monetary Economics, Elsevier, vol. 24(3), pages 455-470, November.
    3. Abel, Andrew B. & Mishkin, Frederic S., 1983. "An integrated view of tests of rationality, market efficiency and the short-run neutrality of monetary policy," Journal of Monetary Economics, Elsevier, vol. 11(1), pages 3-24.
    4. Ali, Mukhtar M, 1979. "Some Evidence of the Efficiency of a Speculative Market," Econometrica, Econometric Society, vol. 47(2), pages 387-392, March.
    5. Golec, Joseph & Tamarkin, Maurry, 1991. "The degree of inefficiency in the football betting market : Statistical tests," Journal of Financial Economics, Elsevier, vol. 30(2), pages 311-323, December.
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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, H. O. Stekler Research Program on Forecasting.
    2. Roger Vergin, 2001. "Overreaction in the NFL point spread market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(5), pages 497-509.
    3. 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, H. O. Stekler Research Program on Forecasting, revised Jan 2007.
    4. Adi Schnytzer & Guy Weinberg, 2008. "Testing for Home Team and Favorite Biases in the Australian Rules Football Fixed-Odds and Point Spread Betting Markets," Journal of Sports Economics, , vol. 9(2), pages 173-190, April.
    5. Roger Vergin, 1998. "The NFL pointspread market revisited: anomaly or statistical aberration?," Applied Economics Letters, Taylor & Francis Journals, vol. 5(3), pages 175-179.
    6. Ziemba, William, 2020. "Parimutuel betting markets: racetracks and lotteries revisited," LSE Research Online Documents on Economics 118873, London School of Economics and Political Science, LSE Library.
    7. Vergin, Roger C. & Sosik, John J., 1999. "No place like home: an examination of the home field advantage in gambling strategies in NFL football," Journal of Economics and Business, Elsevier, vol. 51(1), pages 21-31, January.
    8. 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.
    9. June Buchanan & Yun Shen, 2021. "Gambling and marketing: a systematic literature review using HistCite," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2837-2851, June.
    10. 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.
    11. 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.
    12. Rodney J. Paul & Andrew P. Weinbach, 2005. "Bettor Misperceptions in the NBA," Journal of Sports Economics, , vol. 6(4), pages 390-400, November.
    13. Fragiskos Archontakis & Evan Osborne, 2007. "Playing It Safe? A Fibonacci Strategy for Soccer Betting," Journal of Sports Economics, , vol. 8(3), pages 295-308, June.
    14. 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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