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The day of the week effect in the cryptocurrency market

Author

Listed:
  • Caporale, Guglielmo Maria
  • Plastun, Alex
Abstract
This paper examines the day of the week effect in the cryptocurrency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal–Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.

Suggested Citation

  • Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
  • Handle: RePEc:eee:finlet:v:31:y:2019:i:c:s1544612318304240
    DOI: 10.1016/j.frl.2018.11.012
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    References listed on IDEAS

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    1. Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun & Inna Makarenko, 2014. "The Weekend Effect: A Trading Robot and Fractional Integration Analysis," Discussion Papers of DIW Berlin 1386, DIW Berlin, German Institute for Economic Research.
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    9. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Alex Plastun, 2016. "The weekend effect: an exploitable anomaly in the Ukrainian stock market?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 43(6), pages 954-965, November.
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    More about this item

    Keywords

    Efficient Market Hypothesis; Day of the week effect; Cryptocurrency; Bitcoin; Anomaly; Trading strategy;
    All these keywords.

    JEL classification:

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

    Statistics

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