Seasonality in the Cross-Section of Cryptocurrency Returns

Long, Huaigang, Zaremba, Adam, Demir, Ender, Szczygielski, Kuba and Vasenin, Mikhail (2020) Seasonality in the Cross-Section of Cryptocurrency Returns. Finance Research Letters, 35. p. 101566. ISSN 1544-6123

[img]
Preview
Text
1-s2.0-S154461232030235X-main.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (747kB) | Preview
Official URL: https://doi.org/10.1016/j.frl.2020.101566

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.

Item Type: Article
Uncontrolled Keywords: Cryptocurrencies, Cross-sectional seasonality, Cross-section of returns, Return predictability, Asset pricing
Subjects: N100 Business studies
N300 Finance
N400 Accounting
Department: Faculties > Business and Law > Newcastle Business School
Depositing User: Elena Carlaw
Date Deposited: 23 Jun 2020 09:21
Last Modified: 23 Jun 2020 09:30
URI: http://nrl.northumbria.ac.uk/id/eprint/43547

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics