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The diversification benefits of cryptocurrency factor portfolios: Are they there?

Author

Listed:
  • Weihao Han

    (University of Aberdeen)

  • David Newton

    (University of Bath)

  • Emmanouil Platanakis

    (University of Bath)

  • Haoran Wu

    (University of Bath)

  • Libo Xiao

    (University of Aberdeen)

Abstract

We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.

Suggested Citation

  • Weihao Han & David Newton & Emmanouil Platanakis & Haoran Wu & Libo Xiao, 2024. "The diversification benefits of cryptocurrency factor portfolios: Are they there?," Review of Quantitative Finance and Accounting, Springer, vol. 63(2), pages 469-518, August.
    • Handle: RePEc:kap:rqfnac:v:63:y:2024:i:2:d:10.1007_s11156-024-01260-w
    DOI: 10.1007/s11156-024-01260-w
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    More about this item

    Keywords

    Cryptocurrency factors; Portfolio optimisation; Diversification benefits; Machine learning;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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