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

The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …

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 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 propose a new modeling approach for the cross section of returns. Our method,
Instrumented Principal Component Analysis (IPCA), allows for latent factors and time …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

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 propose a new method for estimating latent asset pricing factors that fit the time series
and cross-section of expected returns. Our estimator generalizes principal component …

Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with
single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of …

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 …