Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns

81 Pages Posted: 5 Mar 2016  

Benjamin Moritz

Ludwig Maximilian University of Munich; Sal. Oppenheim

Tom Zimmermann

Federal Reserve Board

Date Written: March 1, 2016

Abstract

Which variables provide independent information about the cross-section of future returns? Portfolio sorts and Fama-MacBeth regressions cannot easily answer this question when the number of candidate variables is large and when cross-terms might be important. We introduce a new method based on ideas from the machine learning literature that can be used in this context. Applying the method to past-return-based prediction of future returns, short-term returns become the most important predictors. A trading strategy based on our findings has an information ratio twice as high as a Fama-MacBeth regression accounting for two-way interactions. Transaction costs do not explain the results.

Keywords: Cross-sectional asset pricing, Stock market anomalies, Momentum, Machine Learning

JEL Classification: G12

Suggested Citation

Moritz, Benjamin and Zimmermann, Tom, Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns (March 1, 2016). Available at SSRN: https://ssrn.com/abstract=2740751 or http://dx.doi.org/10.2139/ssrn.2740751

Benjamin Moritz

Ludwig Maximilian University of Munich ( email )

Akademiestr. 1/I
Munich, Bavaria 80799
Germany

HOME PAGE: http://www.benjaminmoritz.de

Sal. Oppenheim

Unter Sachsenhausen 2
Cologne
Germany

HOME PAGE: http://www.oppenheim.de/deen/index.htm

Tom Zimmermann (Contact Author)

Federal Reserve Board ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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