Seasonality in the Cross-Section of Cryptocurrency Returns

Huaigang Long, Adam Zaremba (), Ender Demir, Jan Jakub Szczygielski and Mikhail Vasenin

Finance Research Letters, 2020, vol. 35, issue C

Abstract: This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.

Keywords: Cryptocurrencies; Cross-sectional seasonality; Cross-section of returns; Return predictability; Asset pricing (search for similar items in EconPapers)
JEL-codes: G11 G12 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:35:y:2020:i:c:s154461232030235x

DOI: 10.1016/j.frl.2020.101566

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