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Enhanced Global Asset Pricing Factors

Published online by Cambridge University Press:  13 October 2022

Lukas Zimmermann Open the ORCID record for Lukas Zimmermann [Opens in a new window]
Lukas Zimmermann*
Affiliation:
University of Mannheim Business School
*
lukas_zimmermann@outlook.com
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Abstract

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This article constructs and examines enhanced global return factors. I focus on three different enhancement approaches. First, I incorporate information about the covariance structure in the cross-section of stock returns. Second, I employ volatility-reducing techniques in the time series. Third, I exploit diversification benefits. I form six categorical factors by aggregating information from 214 characteristics. Further, I diversify across factors. The enhancement mechanisms are largely successful and when jointly applied increase the optimal Sharpe ratio on average by a factor of 1.96 compared to the traditional factors. My results point to the importance of employing efficient factors in asset pricing studies.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 6 , September 2023 , pp. 2692 - 2731
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

This article greatly benefited from significant contributions of Sebastian Müller. The idea of employing cross-sectional enhancement and volatility scaling on return factors in international markets has been developed in many common discussions for which I am very thankful, as they made this project possible. In that regard, I kindly thank Sebastian Müller for providing the international stock market data and data on international return predictors. I further thank an anonymous referee, Hendrik Bessembinder (the editor), Scott Cederburg (a referee), Ralph Koijen, Simon Rottke, Oliver Spalt, Erik Theissen, Jiri Tresl, and seminar participants at the University of Mannheim for very helpful comments which greatly helped to improve the article. I acknowledge support by the state of Baden-Württemberg through the bwHPC high-performance computing cluster and the German Research Foundation (DFG) through grant INST 35/1134-1 FUGG.

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