Learning and Efficiency in a Gambling Market
AbstractWe 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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Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 40 (1994)
Issue (Month): 10 (October)
market efficiency; optimal learning; Kalman filter;
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- Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Department of Economics, Bar-Ilan University.
- Herman O. Stekler & David Sendor & Richard Verlander, 2009.
"Issues in Sports Forecasting,"
2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
- 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.
- Vaughan Williams, Leighton & Stekler, Herman O., 2010.
International Journal of Forecasting,
Elsevier, vol. 26(3), pages 445-447, July.
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