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

Technologies have spawned finance innovations since the early days of computer
applications in businesses, most recently reaching the stage of disruptive innovations, such …

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 …

Several papers argue that financial economics faces a replication crisis because the
majority of studies cannot be replicated or are the result of multiple testing of too many …

We develop a method that identifies the attention paid by earnings call participants to firms'
climate change exposures. The method adapts a machine learning keyword discovery …

We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su
(KPS, 2019), our model allows for latent factors and factor exposures that depend on …

We examine the effect of environment, social, and governance (ESG) score on stock returns
in the United Kingdom (UK). Consistent with Hong and Kacperczyk (2009), Bolton and …

We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …

We establish that cryptocurrency returns are driven and can be predicted by factors that are
specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency …

We find that three factors—cryptocurrency market, size, and momentum—capture the cross‐
sectional expected cryptocurrency returns. We consider a comprehensive list of price‐and …