We employ a repertoire of machine learning models to explore the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains, those that account for nonlinearities and interactions fare the best. The return predictability derives mainly...

We examine return predictability with machine learning in 46 international stock markets. We calculate 148 stock characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple, yet popular, factor types—such as momentum,...

We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic...