Interacting Anomalies

55 Pages Posted: 5 Aug 2020

Date Written: July 8, 2020

Abstract

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

Müller, Karsten and Schmickler, Simon, Interacting Anomalies (July 8, 2020). Available at SSRN: https://ssrn.com/abstract=3646417 or http://dx.doi.org/10.2139/ssrn.3646417

Karsten Müller

Princeton University

Julis Romo Rabinowitz Building
Washington Road
Princeton, NJ 08544
United States

Simon Schmickler (Contact Author)

Princeton University ( email )

Princeton, NJ
United States

HOME PAGE: http://simonschmickler.com

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