Extrapolation and Bubbles
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.
The authors’ affiliations are Yale School of Management, Harvard Business School, California Institute of Technology, and Harvard University, respectively. We are grateful to Alex Chinco, Charles Nathanson, Alp Simsek, Adi Sunderam, and seminar participants at Berkeley, Caltech, Cornell, Northwestern, Ohio State, Yale, the AEA, the Miami Behavioral Finance Conference, and the NBER for very helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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Last updated December 16, 2015
Nicholas Barberis & Robin Greenwood & Lawrence Jin & Andrei Shleifer, 2018. "Extrapolation and bubbles," Journal of Financial Economics, . citation courtesy of