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A Trend Factor for the Cross Section of Cryptocurrency Returns

Published online by Cambridge University Press:  15 July 2025

Christian Fieberg Open the ORCID record for Christian Fieberg [Opens in a new window] ,
Gerrit Liedtke Open the ORCID record for Gerrit Liedtke [Opens in a new window] ,
Thorsten Poddig ,
Thomas Walker Open the ORCID record for Thomas Walker [Opens in a new window]  and
Adam Zaremba Open the ORCID record for Adam Zaremba [Opens in a new window]
Christian Fieberg
Affiliation:
HSB University of Applied Sciences Bremen, City University of Applied Sciences Concordia University University of Luxembourg christian.fieberg@hs-bremen.de
Gerrit Liedtke
Affiliation:
University of Bremen, Faculty of Business Studies and Economics gliedtke@uni-bremen.de
Thorsten Poddig
Affiliation:
University of Bremen, Faculty of Business Studies and Economics poddig@uni-bremen.de
Thomas Walker
Affiliation:
Concordia University Corvinus Institute for Advanced Studies, Corvinus University of Budapest thomas.walker@concordia.ca
Adam Zaremba*
Affiliation:
MBS School of Business Poznan University of Economics and Business Monash University, Monash Centre for Financial Studies
*
adam.zaremba@ue.poznan.pl or a.zaremba@mbs-education.com (corresponding author)
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Abstract

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We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 60 , Issue 7 , November 2025 , pp. 3116 - 3153
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 (http://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), 2025. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank Lin William Cong, Lingfei Kong, Yukun Liu, Guofu Zhou, an anonymous referee, and Jarrad Harford (the editor) for helpful comments.

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