Seasonality in the Cross-Section of Cryptocurrency Returns

Huaigang Long; Adam Zaremba; Ender Demir; Jan Jakub Szczygielski; Mikhail Vasenin

doi:10.1016/j.frl.2020.101566

Seasonality in the Cross-Section of Cryptocurrency Returns

Huaigang Long, Adam Zaremba^*, Ender Demir, Jan Jakub Szczygielski, Mikhail Vasenin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	101566
Journal	Finance Research Letters
Volume	35
Early online date	12 May 2020
DOIs	https://doi.org/10.1016/j.frl.2020.101566
Publication status	Published - 1 Jul 2020

Keywords

Asset pricing
Cross-section of returns
Cross-sectional seasonality
Cryptocurrencies
Return predictability

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10.1016/j.frl.2020.101566Licence: CC BY-NC-ND

1-s2.0-S154461232030235X-mainFinal published version, 730 KBLicence: CC BY-NC-ND

Cite this

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title = "Seasonality in the Cross-Section of Cryptocurrency Returns",

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 = "Asset pricing, Cross-section of returns, Cross-sectional seasonality, Cryptocurrencies, Return predictability",

author = "Huaigang Long and Adam Zaremba and Ender Demir and Szczygielski, \{Jan Jakub\} and Mikhail Vasenin",

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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

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DO - 10.1016/j.frl.2020.101566

M3 - Article

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SN - 1544-6123

VL - 35

JO - Finance Research Letters

JF - Finance Research Letters

M1 - 101566

ER -

Seasonality in the Cross-Section of Cryptocurrency Returns

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Keywords

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