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 analytically compare two dominant methodologies for the construction of an ESG factor:
the time-series (ratings used to sort stocks) and cross-sectional (ratings used to weight …

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and
applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone …

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 …

This paper studies the heterogeneous effects of subjective macroeconomic expectations on
the cross-section of equity returns. We argue that an upward revision in expectations of …

We document challenges to the notion of a trade-off between systematic risk and expected
returns when analyzing the empirical ability of stock characteristics to predict excess returns …

We propose a novel method to estimate latent asset-pricing factors that incorporate
economic structure. Our estimator generalizes principal component analysis by including …

We construct a news-implied index of greenwashing. Our index reveals that greenwashing
has become particularly prominent in the past five years. Its increase was driven by …

ABSTRACT A war-related factor model derived from textual analysis of media news reports
explains the cross section of expected stock returns. Using a semi-supervised topic model to …