We survey recent methodological contributions in asset pricing using factor models and
machine learning. We organize these results based on their primary objectives: estimating …

We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

We show that two important issues in empirical asset pricing—the presence of weak factors
and the selection of test assets—are deeply connected. Since weak factors are those to …

Portfolios sorted by momentum show stronger return monotonicity than those formed using
other anomalies. Compared with other strategies, the performance of such a momentum …

We develop a statistical framework to learn the high-dimensional stochastic discount factor
(SDF) from a large set of characteristic-based portfolios. Specifically, we build on the …

In macroeconomic forecasting, principal component analysis (PCA) has been the most
prevalent approach to the recovery of factors, which summarize information in a large set of …