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 …

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find
that allowing for nonlinearities significantly increases the out-of-sample performance of …

The pricing of carbon transition risk is central to the debate on climate‐aware investments.
Emissions are tightly linked to sales and are available to investors only with significant lags …

We propose a conditional factor model for corporate bond returns with five factors and time‐
varying factor loadings. We have three main empirical findings. First, our factor model excels …

Identifying the factors that drive the cross-section of expected returns is challenging for at
least three reasons. First, the choice of testing approach (time series versus cross-sectional) …

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 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 …

Perhaps the most widely touted of GPT-4's at-launch, zero-shot capabilities has been its
reported 90th-percentile performance on the Uniform Bar Exam. This paper begins by …