Work

Seasonality in the Cross-Section of Cryptocurrency Returns

Year: 2020

Type: article

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.

Source: Finance research letters

DOI: 10.1016/j.frl.2020.101566

Authors: Huaigang Long, Adam Zaremba, Ender Demir, Jan Jakub Szczygielski, Mikhail Vasenin

Institutions: Zhejiang University, Poznań University of Economics and Business, University of Dubai, Społeczna Akademia Nauk, Istanbul Medeniyet University, Northumbria University, University of Pretoria

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $1,250

OpenAlex ID: W3024233237

FWCI: 8.832

Cites: 37

Cited by: 49

Related to: 10