Learning and Efficiency in a Gambling Market
利用美国国家橄榄球联盟比赛数据和博彩赔率，通过卡尔曼滤波估计球队能力的周际变化，研究市场参与者如何从历史信息中学习，并检验市场效率。
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.