In this paper, we conduct a simulation analysis of the Fama and MacBeth (1973) two-pass procedure, as well as maximum likelihood (ML) and generalized method of moments estimators of cross-sectional expected return models. We also provide some new analytical results on computational issues, the...

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...

We examine the relation between firms’ environmental, social, and governance (ESG) performance and aggregate stock market returns. Based on 38 ESG scores, we construct a market-level ESG index and find its strong positive predictive power on the market returns both in- and out-of-sample, and...

We document empirically that firm fundamentals have explanatory power on the shape of the option implied volatility (IV) curve that is both economically and statistically significant. We find further that, after accounting for fundamentals, the associated IV process can generate overreaction in...

In this paper, we explore what factors drive expected corporate bond returns all over the world. With a novel dataset, and utilizing machine learning models, we find there is strong predictability of corporate bond returns in international markets. However, the documented factors that drive...

We provide a short and selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with major predictors and are primarily interested in the ideas, methods, and their applications. To understand...