Diagnostic Expectations and Stock Returns
We revisit La Porta’s (1996) finding that returns on stocks with the most optimistic analyst long term earnings growth forecasts are substantially lower than those for stocks with the most pessimistic forecasts. We document that this finding still holds, and present several further facts about the joint dynamics of fundamentals, expectations, and returns for these portfolios. We explain these facts using a new model of belief formation based on a portable formalization of the representativeness heuristic. In this model, analysts forecast future fundamentals from the history of earnings growth, but they over-react to news by exaggerating the probability of states that have become objectively more likely. Intuitively, fast earnings growth predicts future Googles but not as many as analysts believe. We test predictions that distinguish this mechanism from both Bayesian learning and adaptive expectations, and find supportive evidence. A calibration of the model offers a satisfactory account of the key patterns in fundamentals, expectations, and returns.
Gennaioli thanks the European Research Council and Shleifer thanks the Pershing Square Venture Fund for Research on the Foundations of Human Behavior for financial support of this research. We are grateful to seminar participants at Brown University and Sloan School, and especially to Josh Schwartzstein, Jesse Shapiro, Pietro Veronesi, and Yang You for helpful comments. We also thank V. V. Chari, who encouraged us to confront our model of diagnostic expectations with the Kalman filter. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
PEDRO BORDALO & NICOLA GENNAIOLI & RAFAEL LA PORTA & ANDREI SHLEIFER, 2019. "Diagnostic Expectations and Stock Returns," The Journal of Finance, vol 74(6), pages 2839-2874. citation courtesy of