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Leveraging Explainable AI to Support Cryptocurrency Investors

Author

Listed:
  • Jacopo Fior

    (Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy)

  • Luca Cagliero

    (Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy)

  • Paolo Garza

    (Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy)

Abstract

In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.

Suggested Citation

  • Jacopo Fior & Luca Cagliero & Paolo Garza, 2022. "Leveraging Explainable AI to Support Cryptocurrency Investors," Future Internet, MDPI, vol. 14(9), pages 1-19, August.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:9:p:251-:d:896429
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    References listed on IDEAS

    as
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    Cited by:

    1. 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.
    2. 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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