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 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 …

Recent studies document strong empirical support for multifactor models that aim to explain
the cross-sectional variation in corporate bond expected excess returns. We revisit these …

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 …

This study provides evidence on the existence of a negative greenium, ie a risk premium
related to the greenness of a firm, based on European individual stock returns. We define a …

Much of the extant literature predicts market returns with “simple” models that use only a few
parameters. Contrary to conventional wisdom, we theoretically prove that simple models …

We propose a model selection method to systematically evaluate the contribution to asset
pricing of any new factor, above and beyond what a high‐dimensional set of existing factors …

We propose a nonparametric method to study which characteristics provide incremental
information for the cross-section of expected returns. We use the adaptive group LASSO to …

We create a newspaper-based Equity Market Volatility (EMV) tracker that moves with the VIX
and with the realized volatility of returns on the S&P 500. Parsing the underlying text, we find …