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

Publisher Summary This chapter provides a selective review of recent work in behavioral
finance. Modern finance assumes that the study of substantively rational solutions to …

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 …

Most anomalies fail to hold up to currently acceptable standards for empirical finance. With
microcaps mitigated via NYSE breakpoints and value-weighted returns, 65% of the 452 …

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 …

Our paper reexamines whether 29 variables from 26 papers published after, as well as the
original 17 variables, were useful in predicting the equity premium in-sample and out-of …

Using a new dataset of environmental, social and corporate governance (ESG) company
ratings and state-of-the-art statistical methodology, this article analyses the performance of …

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 study the out‐of‐sample and post‐publication return predictability of 97 variables shown
to predict cross‐sectional stock returns. Portfolio returns are 26% lower out‐of‐sample and …