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

A great deal of academic empirical research has been published on value and growth
investing. We review and update this literature, discuss the various explanations for the …

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

Hundreds of papers and factors attempt to explain the cross-section of expected returns.
Given this extensive data mining, it does not make sense to use the usual criteria for …

We survey the literature on stock return forecasting, highlighting the challenges faced by
forecasters as well as strategies for improving return forecasts. We focus on US equity …

Academic research relies extensively on macroeconomic variables to forecast the US equity
risk premium, with relatively little attention paid to the technical indicators widely employed …

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 …

If asset returns have systematic skewness, expected returns should include rewards for
accepting this risk. We formalize this intuition with an asset pricing model that incorporates …