Disagreement and Learning: Dynamic Patterns of Trade




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    • Northwestern University and Stanford University, respectively. We thank Anat Admati, Peter DeMarzo, Mike Fishman, Eugene Kandel, Doron Levit, Pedro Saffi, Jiang Wang, and seminar participants at Stanford, the London Business School doctoral conference, and the American Finance Association (2006) Meetings for useful comments.


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.