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Explainable artificial intelligence for crypto asset allocation

Author

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  • Babaei, Golnoosh
  • Giudici, Paolo
  • Raffinetti, Emanuela

Abstract

Many investors have been attracted by Crypto assets in the last few years. However, despite the possibility of gaining high returns, investors bear high risks in crypto markets. To help investors and make the markets more reliable, Robot advisory services are rapidly expanding in the field of crypto asset allocation. Robot advisors not only reduce costs but also improve the quality of the service by involving investors and make the market more transparent. However, the reason behind the given solutions is not clear and users face a black-box model that is complex. The aim of this paper is to improve trustworthiness of robot advisors, to facilitate their adoption. For this purpose, we apply Shapley values to the predictions generated by a machine learning model based on the results of a dynamic Markowitz portfolio optimization model and provide explanations for what is behind the selected portfolio weights.

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  • Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
    • Handle: RePEc:eee:finlet:v:47:y:2022:i:pb:s1544612322002021
    DOI: 10.1016/j.frl.2022.102941
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    References listed on IDEAS

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    1. Ahelegbey, Daniel Felix & Giudici, Paolo & Mojtahedi, Fatemeh, 2021. "Tail risk measurement in crypto-asset markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
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    Citations

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    Cited by:

    1. Jacopo Fior & Luca Cagliero & Paolo Garza, 2022. "Leveraging Explainable AI to Support Cryptocurrency Investors," Future Internet, MDPI, vol. 14(9), pages 1-19, August.
    2. Walid Mensi & Anoop S. Kumar & Hee-Un Ko & Sang Hoon Kang, 2024. "Intraday spillovers in high-order moments among main cryptocurrency markets: the role of uncertainty indexes," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(2), pages 507-538, June.
    3. Fakhfekh, Mohamed & Bejaoui, Azza & Bariviera, Aurelio F. & Jeribi, Ahmed, 2024. "Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    4. Naeem, Muhammad Abubakr & Arfaoui, Nadia & Yarovaya, Larisa, 2025. "The contagion effect of artificial intelligence across innovative industries: From blockchain and metaverse to cleantech and beyond," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    5. Zhou, Linjiang & Shi, Xiaochuan & Bao, Yaxiong & Gao, Lihua & Ma, Chao, 2023. "Explainable artificial intelligence for digital finance and consumption upgrading," Finance Research Letters, Elsevier, vol. 58(PC).
    6. Theo Berger, 2025. "On the information content of explainable artificial intelligence for quantitative approaches in finance," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 47(1), pages 177-203, March.
    7. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    8. José Manuel Carbó Martinez & Sergio Gorjón Rivas, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    9. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    10. Ben Jabeur, Sami & Gozgor, Giray & Rezgui, Hichem & Mohammed, Kamel Si, 2024. "Dynamic dependence between quantum computing stocks and Bitcoin: Portfolio strategies for a new era of asset classes," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    11. Zhuohuan Hu & Richard Yu & Zizhou Zhang & Haoran Zheng & Qianying Liu & Yining Zhou, 2024. "Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms," Papers 2412.18202, arXiv.org, revised Jul 2025.
    12. Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
    13. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
    14. Kovvuri, Veera Raghava Reddy & Fu, Hsuan & Fan, Xiuyi & Seisenberger, Monika, 2023. "Fund performance evaluation with explainable artificial intelligence," Finance Research Letters, Elsevier, vol. 58(PB).
    15. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).

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