Safety First, Learning Under Ambiguity, and the Cross-Section of Stock Returns

Abstract

We examine the empirical implications of learning under ambiguity for the cross-section of stock returns. We introduce a theoretically-motivated ambiguity measure and find that ambiguity is priced in the cross-section of average stock returns. Ambiguity is not subsumed by state variables known to predict stock returns, nor by value, size, and momentum factors. In R-squared comparative tests, a model that takes ambiguity into account performs better than empirical implementations of the Bayesian learning model, the intertemporal CAPM, and the four-factor model of Fama and French (1993) and Carhart (1997).

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