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Financial Markets and Portfolio Management

, Volume 30, Issue 1, pp 19–61 | Cite as

Which stocks drive the size, value, and momentum anomalies and for how long? Evidence from a statistical leverage analysis

  • Kevin AretzEmail author
  • Marc Aretz
Article

Abstract

A large number of neoclassical, behavioral, and bias-based theories try to explain the tendency of small, value, and winner stocks to outperform big, growth, and loser stocks, three well-known characteristic anomalies. Because the theories often predict similar relationships between a stock’s propensity to contribute to the anomalies and a set of correlated firm characteristics, existing studies focusing on single theories do not tell us which theory is most successful in explaining the anomalies. To fill this gap, we use a new non-parametric methodology to run a horse race between the theories. In the first step, we use statistical leverage analysis to find out which stocks are ultimately responsible for the anomalies. In the second, we use the firm characteristics suggested by the theories to forecast the identity of the anomaly drivers, with the purpose of determining which theory is most supported by the data. We find that behavioral theories are most convincing in explaining the size and book-to-market anomalies, while no theory is convincing in explaining the momentum anomaly.

Keywords

Characteristic anomalies Statistical leverage analysis Efficient markets 

JEL Classification

G11 G12 G15 

Notes

Acknowledgments

We are greatly indebted to Markus Schmid (the editor) and an anonymous referee for helping us to improve our work. We are also indebted to Behzet Cengiz, Massimo Guidolin, Peter Pope, Rüdiger von Nitzsch, as well as seminar participants at the ACATIS Value Conference in Frankfurt for their valuable and constructive comments and advice.

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© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Accounting and Finance Division, Manchester Business SchoolThe University of ManchesterManchesterUK
  2. 2.Department of Economics and FinanceRWTH Aachen UniversityAachenGermany

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