59 Pages Posted: 5 Aug 2020 Last revised: 6 Aug 2021
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 that invests in the top backward-looking double-sort strategy generates equal-weighted (value-weighted) monthly average returns of 4% (2.7%) at an annualized Sharpe ratio of 2 (1.38), on par with state-of-the-art anomaly-based machine learning strategies.
Keywords: Stock Market Anomalies, Multiple Testing, Double-Sorted Portfolios, Cross-Section of Returns
JEL Classification: G11, G12
Suggested Citation: Suggested Citation