Extrapolation and Bubbles

by Nicholas Barberis, Robin Greenwood, Lawrence Jin, and Andrei Shleifer
 
 

Overview — Bubble episodes have fascinated economists and historians for centuries, in part because human behavior in bubbles is so hard to explain, and in part because of the devastating side effects of the crash. At the heart of the standard historical narratives of bubbles is the concept of extrapolation—the formation of expected returns by investors based on past returns. This paper presents a simple model of extrapolative bubbles that explains a lot of evidence and makes new predictions.

Author Abstract

We 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. We 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. We present empirical evidence that bears on some of the model’s distinctive predictions.

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