Extrapolation and Bubbles

53 Pages Posted: 1 Feb 2016 Last revised: 18 Dec 2022

See all articles by Nicholas Barberis

Nicholas Barberis

National Bureau of Economic Research (NBER); Yale School of Management

Robin M. Greenwood

Harvard Business School - Finance Unit; National Bureau of Economic Research (NBER)

Lawrence J. Jin

SC Johnson College of Business, Cornell University

Andrei Shleifer

Harvard University - Department of Economics; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI)

Date Written: January 2016

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.

Suggested Citation

Barberis, Nicholas and Barberis, Nicholas and Greenwood, Robin M. and Jin, Lawrence J. and Shleifer, Andrei, Extrapolation and Bubbles (January 2016). NBER Working Paper No. w21944, Available at SSRN: https://ssrn.com/abstract=2725759

Nicholas Barberis (Contact Author)

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Robin M. Greenwood

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Lawrence J. Jin

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Andrei Shleifer

Harvard University - Department of Economics ( email )

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