55 Pages Posted: 5 Aug 2020
Date Written: July 8, 2020
An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy based on double-sorted portfolios performs on par with state-of-the-art machine learning strategies, suggesting that simple combinations of characteristics can capture a similar amount of variation in expected returns.
Keywords: Stock Market Anomalies, Multiple Testing, Double-Sorted Portfolios, Cross-Section of Returns, Machine Learning
JEL Classification: G11, G12
Suggested Citation: Suggested Citation