Firms that concentrate their activities towards goods with higher income elasticity are more exposed to demand-driven risk since the consumption of high-consumption households is more exposed to aggregate shocks. These firms earn higher risk-adjusted equity returns. A portfolio that goes long on...

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...

Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Data mining generates other features of peer review including the rise in...

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....

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...