Bordalo, Pedro, Katie Coffman, Nicola Gennaioli, and Andrei Shleifer. 2019. “
Beliefs about Gender.” American Economic Review 109 (3): 739-773.
Abstract We conduct laboratory experiments that explore how gender stereotypes shape beliefs about ability of oneself and others in different categories of knowledge. The data reveal two patterns. First, men’s and women’s beliefs about both oneself and others exceed observed ability on average, particularly in difficult tasks. Second, overestimation of ability by both men and women varies across categories. To understand these patterns, we develop a model that separates gender stereotypes from mis-estimation of ability related to the difficulty of the task. We find that stereotypes contribute to gender gaps in self-confidence, assessments of others, and behavior in a cooperative game.
PDFWe revisit La Porta's finding that returns on stocks with the most optimistic analyst long‐term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.
PDF Internet AppendixGreenwood, Robin, Andrei Shleifer, and Yang You. 2019. “
Bubbles for Fama.” Journal of Financial Economics 131 (1): 20-43.
Abstract We evaluate Eugene Fama’s claim that stock prices do not exhibit price bubbles. Based on US industry returns 1926-2014 and international sector returns 1985-2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in US and international samples.
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