We use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases...

We propose a new method of testing asset pricing models that relies on using quantities rather than simply prices or returns. We use the capital flows into and out of mutual funds to infer which risk model investors use. We derive a simple test statistic that allows us to infer, from a set of...

Using the value that a mutual fund extracts from capital markets as the measure of skill, we find that the average mutual fund has used this skill to generate about $3.2 million per year. We document large cross-sectional differences in skill that persist for as long as 10 years. We further...

We decompose firm-level corporate bond and equity index returns into (1) duration-matched government bond returns and (2) the excess return over and above this duration-matched counterfactual, what we term duration-adjusted returns. Our decomposition provides markedly different return patterns...