Disagreement and Learning: Dynamic Patterns of Trade
构建了一个动态模型，研究投资者对公共信息解读的分歧如何影响交易量和收益波动，发现偶尔的重大分歧会导致交易量正自相关且与波动正相关，并给出新的实证预测。
ABSTRACT The empirical evidence on investor disagreement and trading volume is difficult to reconcile in standard rational expectations models. We develop a dynamic model in which investors disagree about the interpretation of public information. We obtain a closed‐form linear equilibrium that allows us to study which restrictions on the disagreement process yield empirically observed volume and return dynamics. We show that when investors have infrequent but major disagreements, there is positive autocorrelation in volume and positive correlation between volume and volatility. We also derive novel empirical predictions that relate the degree and frequency of disagreement to volume and volatility dynamics.