We document strong evidence of cross-sectional predictability of corporate bond returns based on a set of yield predictors that capture the information in the yields of past 1, 3, 6, 12, 24, 36 and 48 months. Return predictability is economically and statistically significant, and is robust to...

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

We investigate whether firm fundamentals can explain the shape of option implied volatility (IV)curve. Extending Geske's (1977) compound option model, we link firm fundamentals to the IV curvetheoretically. Using options on all available US-listed companies, we find empirically that...

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant...

In this replication paper, we extend Kelly, Malamud, and Pedersen (2021)'s new asset pricing framework to allow incorporating multiple predictive signals into optimal principal portfolios. Empirically, we find that the multi-signal theory is valuable for combining signals, improving a naive...

We document that the first and third cross-sectional moments of corporate bond returns significantly and positively predict future stock market returns both in- and out-of-sample. The predictability emerges from informed bond trading and gradual diffusion of information. Particularly, the...

We uncover a negative correlation between macroeconomic uncertainty and security analyst earning forecasts dispersion, and explain it through herding behavior bias of the analysts. We find that the herding firms, whose analysts suffer the herding bias, have greater firm-level uncertainty than...

We identify factors from a large set of anomalies for explaining hedge fund returns using machine learning methods. Our new model combines anomaly factors with market and macro factors and outperforms existing models both in-sample and out-of-sample. Moreover, the model leads to a significant...

In this paper, we uncover the first momentum pattern of corporate bonds. In contrast to the popular stock momentum, originated by Jegadeesh and Titman (1993) but unextendable to bonds, our momentum is based on the risk components of the bonds rather than past returns. We find that bonds with...

We investigate the effect of ETF ownership on stock market anomalies and market efficiency. We find that low ETF ownership stocks exhibit higher returns, greater Sharpe ratios, and highly significant alphas in comparison to high ETF ownership stocks. We show that high ETF ownership stocks...