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Bitcoin replication using machine learning

Author

Listed:
  • Harris, Richard D.F.
  • Mazibas, Murat
  • Rambaccussing, Dooruj

Abstract

Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.

Suggested Citation

  • Harris, Richard D.F. & Mazibas, Murat & Rambaccussing, Dooruj, 2024. "Bitcoin replication using machine learning," International Review of Financial Analysis, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:finana:v:93:y:2024:i:c:s105752192400139x
    DOI: 10.1016/j.irfa.2024.103207
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    More about this item

    Keywords

    Portfolio replication; Cryptocurrencies; Bitcoin; Machine learning algorithms;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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