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Explainable artificial intelligence for crypto asset allocation

Author

Listed:
  • 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.

Suggested Citation

  • 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).
    2. Paolo Giudici & Emanuela Raffinetti, 2020. "Lorenz Model Selection," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 754-768, October.
    3. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    4. Ahmad Chokor & Élise Alfieri, 2021. "Long and short-term impacts of regulation in the cryptocurrency market," Post-Print hal-03275473, HAL.
    5. Chokor, Ahmad & Alfieri, Elise, 2021. "Long and short-term impacts of regulation in the cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 157-173.
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    8. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
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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. 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.
    3. 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).

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