IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v35y2020ics154461232030235x.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this article

Seasonality in the Cross-Section of Cryptocurrency Returns

Author

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

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.

Suggested Citation

  • Long, Huaigang & Zaremba, Adam & Demir, Ender & Szczygielski, Jan Jakub & Vasenin, Mikhail, 2020. "Seasonality in the Cross-Section of Cryptocurrency Returns," Finance Research Letters, Elsevier, vol. 35(C).
    • Handle: RePEc:eee:finlet:v:35:y:2020:i:c:s154461232030235x
    DOI: 10.1016/j.frl.2020.101566
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232030235X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2020.101566?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    2. Grobys, Klaus & Sapkota, Niranjan, 2019. "Cryptocurrencies and momentum," Economics Letters, Elsevier, vol. 180(C), pages 6-10.
    3. Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
    4. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    5. Hirshleifer, David & Jiang, Danling & DiGiovanni, Yuting Meng, 2020. "Mood beta and seasonalities in stock returns," Journal of Financial Economics, Elsevier, vol. 137(1), pages 272-295.
    6. Vincent Bogousslavsky, 2016. "Infrequent Rebalancing, Return Autocorrelation, and Seasonality," Journal of Finance, American Finance Association, vol. 71(6), pages 2967-3006, December.
    7. Steven L. Heston & Robert A. Korajczyk & Ronnie Sadka, 2010. "Intraday Patterns in the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1369-1407, August.
    8. Baur, Dirk G. & Cahill, Daniel & Godfrey, Keith & (Frank) Liu, Zhangxin, 2019. "Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume," Finance Research Letters, Elsevier, vol. 31(C), pages 78-92.
    9. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    10. Heston, Steven L. & Sadka, Ronnie, 2008. "Seasonality in the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 87(2), pages 418-445, February.
    11. Kaiser, Lars, 2019. "Seasonality in cryptocurrencies," Finance Research Letters, Elsevier, vol. 31(C).
    12. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2019. "Common Risk Factors in Cryptocurrency," NBER Working Papers 25882, National Bureau of Economic Research, Inc.
    13. Aharon, David Yechiam & Qadan, Mahmoud, 2019. "Bitcoin and the day-of-the-week effect," Finance Research Letters, Elsevier, vol. 31(C).
    14. Li, Fengyun & Zhang, Huacheng & Zheng, Dazhi, 2018. "Seasonality in the cross section of stock returns: Advanced markets versus emerging markets," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 263-281.
    15. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    16. Obryan Poyser, 2019. "Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 29-60, March.
    17. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    18. Zaremba, Adam, 2019. "Cross-sectional seasonalities in international government bond returns," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 80-94.
    19. Heston, Steven L. & Sadka, Ronnie, 2010. "Seasonality in the Cross Section of Stock Returns: The International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(5), pages 1133-1160, October.
    20. Wei, Wang Chun, 2018. "Liquidity and market efficiency in cryptocurrencies," Economics Letters, Elsevier, vol. 168(C), pages 21-24.
    21. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    22. Matti Keloharju & Juhani T. Linnainmaa & Peter Nyberg, 2016. "Return Seasonalities," Journal of Finance, American Finance Association, vol. 71(4), pages 1557-1590, August.
    23. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    24. Liu, Weiyi & Liang, Xuan & Cui, Guowei, 2020. "Common risk factors in the returns on cryptocurrencies," Economic Modelling, Elsevier, vol. 86(C), pages 299-305.
    25. Brennan, Michael J. & Chordia, Tarun & Subrahmanyam, Avanidhar, 1998. "Alternative factor specifications, security characteristics, and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 49(3), pages 345-373, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    2. Zaremba, Adam & Bilgin, Mehmet Huseyin & Long, Huaigang & Mercik, Aleksander & Szczygielski, Jan J., 2021. "Up or down? Short-term reversal, momentum, and liquidity effects in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 78(C).
    3. Ahn, Yongkil & Kim, Dongyeon, 2021. "Emotional trading in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 42(C).
    4. Aharon, David Y. & Demir, Ender & Lau, Chi Keung Marco & Zaremba, Adam, 2022. "Twitter-Based uncertainty and cryptocurrency returns," Research in International Business and Finance, Elsevier, vol. 59(C).
    5. Milan Fičura, 2023. "Impact of size and volume on cryptocurrency momentum and reversal," FFA Working Papers 5.003, Prague University of Economics and Business, revised 05 Apr 2023.
    6. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    7. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    8. Shanaev, Savva & Ghimire, Binam, 2022. "A generalised seasonality test and applications for cryptocurrency and stock market seasonality," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 172-185.
    9. Bing Xiao & Philippe Maillebuau, 2020. "The Seasonal Effect On The Chinese Gold Market Using An Empirical Analysis Of The Shanghai Gold Exchange," Post-Print hal-02905216, HAL.
    10. Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Guo, Shuxin & Yuan, Yue & Ma, Feng, 2022. "Cross-sectional seasonalities and seasonal reversals: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 82(C).
    12. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    13. Ahn, Yongkil & Kim, Dongyeon, 2023. "Visceral emotions and Bitcoin trading," Finance Research Letters, Elsevier, vol. 51(C).
    14. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    15. Mercik, Aleksander & Cupriak, Daniel & Zaremba, Adam, 2023. "Factor seasonalities: International and further evidence," Finance Research Letters, Elsevier, vol. 58(PA).
    16. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    17. Shen, Zhuyi & Wang, Shibo & Yang, Jinqiang, 2023. "A note on the dynamic adoption and valuation theory in tokenomics," Finance Research Letters, Elsevier, vol. 56(C).
    18. Assaf, Ata & Bilgin, Mehmet Huseyin & Demir, Ender, 2022. "Using transfer entropy to measure information flows between cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zaremba, Adam & Bilgin, Mehmet Huseyin & Long, Huaigang & Mercik, Aleksander & Szczygielski, Jan J., 2021. "Up or down? Short-term reversal, momentum, and liquidity effects in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 78(C).
    2. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    3. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    4. Guo, Shuxin & Yuan, Yue & Ma, Feng, 2022. "Cross-sectional seasonalities and seasonal reversals: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 82(C).
    5. Mercik, Aleksander & Cupriak, Daniel & Zaremba, Adam, 2023. "Factor seasonalities: International and further evidence," Finance Research Letters, Elsevier, vol. 58(PA).
    6. Long, Huaigang & Zaremba, Adam & Zhou, Wenyu & Bouri, Elie, 2022. "Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns," Journal of Financial Markets, Elsevier, vol. 61(C).
    7. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    8. Keloharju, Matti & Linnainmaa, Juhani T. & Nyberg, Peter, 2021. "Are return seasonalities due to risk or mispricing?," Journal of Financial Economics, Elsevier, vol. 139(1), pages 138-161.
    9. Grobys, Klaus & Junttila, Juha, 2021. "Speculation and lottery-like demand in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    10. Zaremba, Adam, 2019. "Cross-sectional seasonalities in international government bond returns," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 80-94.
    11. Song, Jian & Balvers, Ronald J., 2022. "Seasonality and momentum across national equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    12. Zaremba, Adam & Cakici, Nusret & Bianchi, Robert J. & Long, Huaigang, 2023. "Interest rate changes and the cross-section of global equity returns," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    13. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, April.
    14. Li, Fengyun & Zhang, Huacheng & Zheng, Dazhi, 2018. "Seasonality in the cross section of stock returns: Advanced markets versus emerging markets," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 263-281.
    15. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    16. Zaremba, Adam & Szyszka, Adam & Karathanasopoulos, Andreas & Mikutowski, Mateusz, 2021. "Herding for profits: Market breadth and the cross-section of global equity returns," Economic Modelling, Elsevier, vol. 97(C), pages 348-364.
    17. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    18. Atilgan, Yigit & Demirtas, K. Ozgur & Gunaydin, A. Doruk & Kirli, Imra, 2023. "Mood seasonality around the globe," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    19. Long, Huaigang & Chiah, Mardy & Zaremba, Adam & Umar, Zaghum, 2024. "Changes in shares outstanding and country stock returns around the world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    20. Stereńczak, Szymon & Zaremba, Adam & Umar, Zaghum, 2020. "Is there an illiquidity premium in frontier markets?," Emerging Markets Review, Elsevier, vol. 42(C).

    More about this item

    Keywords

    Cryptocurrencies; Cross-sectional seasonality; Cross-section of returns; Return predictability; Asset pricing;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:35:y:2020:i:c:s154461232030235x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.