Trading under uncertainty about other market participants
构建了一个不对称信息模型，研究不仅对基本面、还对市场中知情交易者比例存在不确定性和学习时的影响，发现极端消息增加两类不确定性、降低价格信息含量，而市场构成不确定性构成流动性风险并伴随高预期收益，模型动态扩展还产生了动量效应和历史依赖的波动性。
Abstract I present an asymmetric information model of financial markets in which there is uncertainty and learning not only about fundamentals but also about the proportion of informed‐to‐noise traders in the market. Extreme news leads to an increase in both types of uncertainty, while it decreases price informativeness. Uncertainty about the market composition constitutes a type of liquidity risk and is associated with high expected returns. The resulting price–volume relationship is U‐shaped and positively sloped. In a dynamic extension of the model I show that this mechanism generates momentum as well as history‐dependent volatility and price informativeness.