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 document challenges to the notion of a trade-off between systematic risk and expected returns when analyzing the empirical ability of stock characteristics to predict excess returns. First, we measure individual stocks' exposures to all common latent factors using a novel high-dimensional...

A large percentage of text in short-sell research reports pertains to accounting fraud and earnings mismanagement. Using survey cash-flow forecasts as a counterfactual, we find that investors underreact to the cash-flow news contained in short-sell reports. On average, target firms earn abnormal...

To examine whether theory helps predict the cross-section of returns, we combine text analysis of publications with out-of-sample tests. Based on the original texts, only 18% of predictors are attributed to risk-based theory. 59% are attributed to mispricing, and 23% have uncertain origins....