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Cryptocurrency Trading Using Machine Learning

Author

Listed:
  • Thomas E. Koker

    (Worcester Polytechnic Institute, Worcester, MA 01609, USA)

  • Dimitrios Koutmos

    (Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University–Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.

Suggested Citation

  • Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:8:p:178-:d:396989
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    References listed on IDEAS

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    Cited by:

    1. Jacopo Fior & Luca Cagliero & Paolo Garza, 2022. "Leveraging Explainable AI to Support Cryptocurrency Investors," Future Internet, MDPI, vol. 14(9), pages 1-19, August.
    2. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    3. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    4. Isabela Ruiz Roque da Silva & Eli Hadad Junior & Pedro Paulo Balbi, 2022. "Cryptocurrencies trading algorithms: A review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1661-1668, December.
    5. Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022. "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).

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