Cryptocurrency Replication Using Machine Learning

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 cryptocurrency portfolios that replicate the risk-adjusted return profile and diversification benefits of cryptocurrencies. We show that synthetic cryptocurrency portfolios offer a compelling alternative to direct investment in cryptocurrencies, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding cryptocurrencies directly. Furthermore, synthetic cryptocurrency portfolios provide better diversification benefits and lower tail risk.

Keywords: Portfolio replication, Cryptocurrencies, Machine learning algorithms