TY - JOUR
T1 - Seasonality in the Cross-Section of Cryptocurrency Returns
AU - Long, Huaigang
AU - Zaremba, Adam
AU - Demir, Ender
AU - Szczygielski, Jan Jakub
AU - Vasenin, Mikhail
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - Asset pricing
KW - Cross-section of returns
KW - Cross-sectional seasonality
KW - Cryptocurrencies
KW - Return predictability
UR - http://www.scopus.com/inward/record.url?scp=85085158906&partnerID=8YFLogxK
U2 - 10.1016/j.frl.2020.101566
DO - 10.1016/j.frl.2020.101566
M3 - Article
AN - SCOPUS:85085158906
SN - 1544-6123
VL - 35
JO - Finance Research Letters
JF - Finance Research Letters
M1 - 101566
ER -