Asset Price Dynamics with Limited Attention
通过一个投资者随机到达交易的模型，识别出长期存在的定价错误，并利用纽约证券交易所的做市商库存、零售订单流和价格数据进行了结构估计。
Abstract We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model’s parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4$\%$, 7.0$\%$, and 4.5$\%$ of the respective daily, monthly, and quarterly idiosyncratic return variances.