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 provide data and code that successfully reproduces nearly all cross-sectional stock
return predictors. Our 319 characteristics draw from previous meta-studies, but we differ by …

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 build cross-sections of asset returns for a given set of characteristics, that is, managed
portfolios serving as test assets, as well as building blocks for tradable risk factors. We use …

We propose that investment strategies should be evaluated based on their net-oftrading-cost
return for each level of risk, which we term the “implementable efficient frontier.” While …

A groundbreaking, authoritative introduction to how machine learning can be applied to
asset pricing Investors in financial markets are faced with an abundance of potentially value …

Purpose This paper aims to determine the effect that human capital and key competitive
drivers such as quality, agility, and cost have on firm performance, whether this effect is …

This article presents an augmented deep factor model that generates latent factors for cross-
sectional asset pricing. The conventional security sorting on firm characteristics for …

The cross-section and time series of stock returns contains a wealth of information about the
stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical …