Using deep learning conditional value-at-risk based utility function in cryptocurrency portfolio optimisation

Huang, X ORCID: https://orcid.org/0009-0003-8394-7573, Tan, L ORCID: https://orcid.org/0000-0001-6986-1923, Su, H ORCID: https://orcid.org/0000-0002-7448-2730 and Cheah, J ORCID: https://orcid.org/0000-0003-2953-3815, 2025. Using deep learning conditional value-at-risk based utility function in cryptocurrency portfolio optimisation. International Journal of Finance and Economics. ISSN 1076-9307 (Forthcoming)

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Abstract

One of the critical risks associated with cryptocurrency assets is the so-called downside risk, or tail risk. Conditional Value-at-Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio. In this paper, we propose a new approach to portfolio construction based on a deep learning CVaR utility function. This approach is designed to address the issue of tail risk. We evaluate the performance of this approach in comparison to other portfolio construction techniques, including the naïve, minimum variance, and mean-variance portfolios. Our findings indicate that the proposed approach outperforms traditional optimisation models.

Item Type:	Journal article
Publication Title:	International Journal of Finance and Economics
Creators:	Huang, X., Tan, L., Su, H. and Cheah, J.
Publisher:	Wiley
Date:	2 July 2025
ISSN:	1076-9307
Identifiers:	Number Type 2464599 Other
Divisions:	Schools > Nottingham Business School
Record created by:	Jonathan Gallacher
Date Added:	07 Jul 2025 15:27
Last Modified:	07 Jul 2025 15:27
URI:	https://irep.ntu.ac.uk/id/eprint/53893

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