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).

“Structured products” (SP) have recently been introduced onto organized markets in the United States. Payoffs for such financially-engineered securities typically combine stock, bond and contingent claims features. In this paper attention is focused upon reverse-exchangeable securities (RES), an important and rapidly growing segment of the American SP market. First we undertake a replication of RES payout with a linear portfolio of publicly traded securities in a simple no-arbitrage framework. It is thereby possible to estimate theoretically “fair” terms of issuance, and contrast these with actual terms. We conclude that there is a significant pricing bias in favor of the issuing financial institution. Credit enhancement resulting from observed positive correlation between the RES terminal payoff and issuer financial performance is proposed as explanation for the apparent pricing discrepancy. Market completeness and possible tax advantages may also play a role in SP demand and the rapid expansion of this new derivatives market.