 Bitcoin replication using machine learningRichard D.F. Harris, Murat Mazibaş and Dooruj Rambaccussing International Review of Financial Analysis, 2024, vol. 93, issue C 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. Keywords: Portfolio replication; Cryptocurrencies; Bitcoin; Machine learning algorithms (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:93:y:2024:i:c:s105752192400139x DOI: 10.1016/j.irfa.2024.103207 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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