We study how 9 different market participants trade with respect to 130 different stock return anomalies and how each participant's trades predict returns. Retail investors trade against anomalies, while firms' and short sellers' trades agree with anomalies. Institutional portfolios are weighted...

We investigate the cross-sectional return predictability of delta-hedged equity options using machine learning and big data. Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample...

We introduce a new, hybrid measure of stock return tail covariance risk, motivated by the under-diversified portfolio holdings of individual investors, and investigate its cross-sectional predictive power. Our key innovation is that this covariance is measured across the left tail states of the...

We investigate whether the distributional characteristics of corporate bonds predict the cross-sectional differences in future bond returns. The results indicate a significantly positive (negative) link between volatility (skewness) and expected returns, whereas kurtosis does not make a robust...

We quantify disagreement about the economy with ex-ante measures of divergence of opinion among economic forecasters and investigate if economic disagreement has a significant impact on the cross-sectional pricing of individual stocks. We find a significant disagreement premium of 7.2% per...

We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that...