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 …

The latest development in empirical Asset Pricing is the use of Machine Learning methods to
address the problem of the factor zoo. These techniques offer great flexibility and prediction …

We directly optimize the objectives of portfolio management via deep reinforcement learning-
--an alternative to conventional supervised-learning paradigms that routinely entail first-step …

Complexity in Factor Pricing Models Page 1 Complexity in Factor Pricing Models Antoine
Didisheim Shikun (Barry) Ke Bryan Kelly Semyon Malamud Uni. Melbourne Yale Yale EPFL AFA …

Deep learning technology is rapidly adopted in financial market settings. Using a large data
set from the Chinese stock market, we propose a return-risk trade-off strategy via a new …

Explaining stock predictions is generally a difficult task for traditional non-generative deep
learning models, where explanations are limited to visualizing the attention weights on …

This study employs individual stock returns to examine the evolution of the greenium, which
is the risk premium linked to firms' carbon emissions and environmental transparency. We …

We employ a repertoire of machine learning models to investigate the cross-sectional return
predictability in cryptocurrency markets. While all methods generate substantial economic …

We open up the black box behind Deep Learning for portfolio optimization and prove that a
sufficiently wide and arbitrarily deep neural network (DNN) trained to maximize the Sharpe …

We develop a novel methodology for extracting information from option implied volatility (IV)
surfaces for the cross-section of stock returns, using image recognition techniques from …