Lawrence J. Jin

California Institute of Technology
1200 E. California Blvd. MC 228-77
Pasadena, CA 91125

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Institutional Affiliation: California Institute of Technology

NBER Working Papers and Publications

May 2020Prospect Theory and Stock Market Anomalies
with Nicholas C. Barberis, Baolian Wang: w27155
We present a new model of asset prices in which investors evaluate risk according to prospect theory and examine its ability to explain 22 prominent stock market anomalies. The model incorporates all the elements of prospect theory, takes account of investors' prior gains and losses, and makes quantitative predictions about an asset's average return based on empirical estimates of its volatility, skewness, and past capital gain. We find that the model is helpful for thinking about a majority of the 22 anomalies.
April 2019Reflexivity in Credit Markets
with Robin Greenwood, Samuel G. Hanson: w25747
Reflexivity is the idea that investors' biased beliefs affect market outcomes, and that market outcomes in turn affect investors' beliefs. We develop a behavioral model of the credit cycle featuring such a two-way feedback loop. In our model, investors form beliefs about firms' creditworthiness, in part, by extrapolating past default rates. Investor beliefs influence firms' actual creditworthiness because firms that can refinance maturing debt on favorable terms are less likely to default in the short-run—even if fundamentals do not justify investors' generosity. Our model is able to match many features of credit booms and busts, including the imperfect synchronization of credit cycles with the real economy, the negative relationship between past credit growth and the future return on risk...
January 2016Extrapolation and Bubbles
with Nicholas Barberis, Robin Greenwood, Andrei Shleifer: w21944
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.

Published: Nicholas Barberis & Robin Greenwood & Lawrence Jin & Andrei Shleifer, 2018. "Extrapolation and bubbles," Journal of Financial Economics, . citation courtesy of

June 2013X-CAPM: An Extrapolative Capital Asset Pricing Model
with Nicholas Barberis, Robin Greenwood, Andrei Shleifer: w19189
Survey evidence suggests that many investors form beliefs about future stock market returns by extrapolating past returns: they expect the stock market to perform well (poorly) in the near future if it performed well (poorly) in the recent past. Such beliefs are hard to reconcile with existing models of the aggregate stock market. We study a consumption-based asset pricing model in which some investors form beliefs about future price changes in the stock market by extrapolating past price changes, while other investors hold fully rational beliefs. We find that the model captures many features of actual prices and returns, but is also consistent with the survey evidence on investor expectations. This suggests that the survey evidence does not need to be seen as an inconvenient obstacle to u...

Published: Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2015. "X-CAPM: An extrapolative capital asset pricing model," Journal of Financial Economics, Elsevier, vol. 115(1), pages 1-24. citation courtesy of

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