Learning and Efficiency in a Gambling Market
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.
Volume (Year): 40 (1994)
Issue (Month): 10 (October)
|Contact details of provider:|| Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA|
Web page: http://www.informs.org/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:40:y:1994:i:10:p:1317-1328. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc)
If references are entirely missing, you can add them using this form.