NBER Publications by Jonathan Lewellen

It has become standard practice in the cross-sectional asset-pricing literature to evaluate models based on how well they explain average returns on size- and B/M-sorted portfolios, something many models seem to do remarkably well. In this paper, we review and critique the empirical methods used in the literature. We argue that asset-pricing tests are often highly misleading, in the sense that apparently strong explanatory power (high cross-sectional R2s and small pricing errors) in fact provides quite weak support for a model. We offer a number of suggestions for improving empirical tests and evidence that several proposed models don%u2019t work as well as originally advertised.

Recent studies suggest that the conditional CAPM might hold, period-by-period, and that time-varying betas can explain the failures of the simple, unconditional CAPM. We argue, however, that significant departures from the unconditional CAPM would require implausibly large time-variation in betas and expected returns. Thus, the conditional CAPM is unlikely to explain asset-pricing anomalies like book-to-market and momentum. We test this conjecture empirically by directly estimating conditional alphas and betas from short-window regressions (avoiding the need to specify conditioning information). The tests show, consistent with our analytical results, that the conditional CAPM performs nearly as poorly as the unconditional CAPM.

In asset pricing, estimation risk refers to investor uncertainty about the parameters of the return or cashflow process. We show that with estimation risk the observable properties of prices and returns can differ significantly from the properties perceived by rational investors. In particular, parameter uncertainty will tend to induce return predictability in ways that resemble irrational mispricing, and prices can violate familiar volatility bounds when investors are rational. Cross-sectionally, expected returns deviate from the CAPM even if investors attempt to hold mean-variance efficient portfolios, and these deviations can be predictable based on past dividends and prices. In short, estimation risk can be important for characterizing and testing market efficiency.