How Important Is Mispricing?

Despite abundant evidence that firms’ characteristics predict their asset returns, we know little about how much firms’ asset prices deviate from their true values. Such mispricing could be distinct from observed return predictability if investors have biased beliefs that are not highly correlated with firms’ characteristics. We use a model to estimate the extent of information processing biases that would reproduce empirical asset pricing anomalies. Our findings indicate that the magnitude of mispricing is several times larger than observed anomalies suggest. The model also provides novel insights into when and how information processing biases can cause substantial capital misallocation. * We thank UT Austin and Columbia for research support. We appreciate helpful suggestions from Sheridan Titman and seminar participants at UT Austin and UNC Chapel Hill. Please send all correspondence to [email protected] and [email protected]. Although many studies document predictability in firms’ stock returns, it remains unclear from this evidence how close firms’ asset prices are to their fundamental values. In behavioral theories, the degree of mispricing depends on certain biases in investors’ beliefs that researchers cannot directly observe. If these biases in investors’ beliefs are not highly correlated with firms’ characteristics, return predictability from characteristics will differ from true mispricing. To explore this possibility, we analyze a parsimonious model in which observed return predictability comes from information processing biases, rather than compensation for risk. The model allows us to quantify the asset mispricing implied by behavioral theories of information processing biases. We estimate the extent of biases that would produce observed asset return predictability coming from firms’ valuation ratios, investment rates, and cash flows. This analysis reveals that observed return anomalies are the tip of the iceberg: insofar as behavioral biases cause anomalies, unobservable asset mispricing is much larger than its observable proxies, such as the value anomaly. The reason is that empirical anomalies are poor proxies for the errors in investors’ beliefs that determine the degree of mispricing. For example, although a firm’s Tobin’s Q is a common proxy for mispricing, only some of the variation in Q comes from errors in investors’ beliefs. Much of the variation comes from the rational anticipation of varying firm productivity. Model simulations show that sorting by firms’ (true) expected returns to a rational agent produces a difference in expected returns that is three times larger than the difference produced by sorting by firms’ Q values. In the benchmark model, managers who determine firm investment decisions and investors who value firm assets have the same beliefs. Firm productivity varies over time, but 1 Respectively, Banz (1981), Bernard and Thomas (1989), and Fama and French (1992) show that stocks with small sizes, positive earnings news, and high ratios of book-to-market equity have positive risk-adjusted returns. 2 Examples of theories in which errors in investors’ expectations lead to return predictability include Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subramanyam (1998), and Hong and Stein (1999).