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...

I provide an economic model that justifies using bag-of-words, topic modeling, and machine learning techniques to measure firms' risk exposures using the percentage they allocate to each risk in their financial statements. The model provides a theoretical set of sufficient conditions under...

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 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...

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...