We propose three principles for evaluating the practical efficacy of machine learning for stock selection, and we compare the performance of various models and investment goals using this framework. The first principle is investability. To this end, we focus on portfolios formed from highly...

Findings on momentum and reversal effects in the stock market are often disparate. Differences in methodology, calibration, data universe, and the granularity of tests have made it challenging to reconcile and unify the various documented relationships. Our goal is to attribute stock return...

The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black box”, demanding interpretation and additional explanation. In this paper, we present a...