Nicholas Barberis; Robin Greenwood and Andrei Shleifer, Harvard University and NBER; and Lawrence Jin, California Institute of Technology

Extrapolation and Bubbles

Barberis, Greenwood, Jin, and Shleifer present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals — an average of the asset's past price changes and the asset's degree of overvaluation. The two signals are in conflict, and investors "waver" over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. The researchers analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. The authors present empirical evidence that bears on some of the model's distinctive predictions.