Reassessing herding in cryptocurrency markets

Christine Jeanneaux (), Elise Alfieri (), Radu Burlacu () and Sonia Jimenez Garces
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Christine Jeanneaux: ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes
Elise Alfieri: IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel
Radu Burlacu: CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes
Sonia Jimenez Garces: CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes

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Abstract: The present study aims to evaluate the efficacy of existing models in detecting herding behaviors in cryptocurrency markets. The extant literature has employed various methods to assess the presence of herding behavior, mobilizing variables such as the cross-sectional dispersion of returns and the cross-sectional dispersion of assets' sensitivities to the market (beta herding). However, these detection models may not exclusively detect herding behaviors. They may also identify rational reactions to the arrival of new information or changes in investors' risk-aversion. To test this proposition, we incorporate factors accounting for new information arrival and time-varying investors' risk-aversion in classical herding models. Our results do not provide support for the presence of herding behaviors in cryptocurrency markets; rather, they underscore the significant impact of informational and risk aversion factors in explaining the cross-sectional deviation of returns.

Date: 2025-11
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Published in Finance Research Letters, 2025, 85, pp.107915. ⟨10.1016/j.frl.2025.107915⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05542613

DOI: 10.1016/j.frl.2025.107915

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