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

This paper surveys the field of asset pricing. The emphasis is on the interplay between
theory and empirical work and on the trade‐off between risk and return. Modern research …

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 use deep neural networks to estimate an asset pricing model for individual stock returns
that takes advantage of the vast amount of conditioning information, keeps a fully flexible …

We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory
power of a large number of cross-sectional stock return predictors. Our method achieves …

We propose a theoretically motivated factor model based on investor psychology and
assess its ability to explain the cross-section of US equity returns. Our factor model …

Using the news-based measure of Baker et al.[Baker SR, Bloom N, Davis SJ (2013)
Measuring economic policy uncertainty. Working paper, Stanford University, Stanford, CA] to …

The last several decades have witnessed a shift away from a fully rational paradigm of
financial markets toward one in which investor behavior is influenced by psychological …

A common approach to comparing asset pricing models involves a competition in pricing
test-asset returns. In contrast, we show that for models with traded factors, when the …

We find consistent value and momentum return premia across eight diverse markets and
asset classes, and a strong common factor structure among their returns. Value and …