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Momentum effects in the cryptocurrency market after one-day abnormal returns

Author

Listed:
  • Guglielmo Maria Caporale

    (Brunel University
    CESifo and DIW Berlin)

  • Alex Plastun

    (Sumy State University)

Abstract

This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.

Suggested Citation

  • Guglielmo Maria Caporale & Alex Plastun, 2020. "Momentum effects in the cryptocurrency market after one-day abnormal returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 251-266, September.
    • Handle: RePEc:kap:fmktpm:v:34:y:2020:i:3:d:10.1007_s11408-020-00357-1
    DOI: 10.1007/s11408-020-00357-1
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    Cited by:

    1. Guglielmo Maria Caporale & Alex Plastun, 2020. "Abnormal Returns and Stock Price Movements: Some Evidence from Developed and Emerging Markets," CESifo Working Paper Series 8783, CESifo.
    2. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    3. Plastun, Alex & Bouri, Elie & Gupta, Rangan & Ji, Qiang, 2022. "Price effects after one-day abnormal returns in developed and emerging markets: ESG versus traditional indices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    4. Reganti Lavanya & Rajesh Mamilla, 2023. "Bibliometric Characteristics of Cryptocurrency through Citation Network Analysis," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(2), pages 46-74, June.
    5. Tom Liu & Stefan Zohren, 2023. "Multi-Factor Inception: What to Do with All of These Features?," Papers 2307.13832, arXiv.org.
    6. Guglielmo Maria Caporale & Alex Plastun, 2021. "Gold and oil prices: abnormal returns, momentum and contrarian effects," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 353-368, September.
    7. Plastun, Alex & Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2021. "Evolution of price effects after one-day abnormal returns in the US stock market," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. 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).
    9. Borgards, Oliver, 2021. "Dynamic time series momentum of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    Cryptocurrency; Anomalies; Momentum effect; Abnormal returns; Patterns;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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