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Bayesian Network in Machine Learning: An Empirical Investigation to Assess the Price Clustering Model During Crises

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  • Fatma Hachicha

    (University of Sfax, Institute of High Business Studies of Sfax)

  • Nada Suissi

    (University of Sfax, Institute of High Business Studies of Sfax)

  • Amine Lahiani

    (LEO-Laboratoire d’Economie d’Orleans
    Gulf University for Science and Technology, Center for Sustainable Development)

Abstract

This paper aims to predict cryptocurrency price clustering by examining the causal relationship between Bitcoin price clustering and several key variables, namely rational investor sentiment (RSI), Bitcoin return, Bitcoin transaction volume, Geopolitical Risk (GPR), Economic Policy Uncertainty (EPU), and irrational investor sentiment (IRSI) using the Bayesian Network methodology. The data cover the period from January 1, 2019, to February 1, 2023, and encompass two major crises, especially the COVID-19 pandemic (from March 11, 2020, to February 23, 2022) and the Russian invasion of Ukraine (from February 22, 2022, to the present). Findings indicate that all the key variables have a direct impact on Bitcoin price clustering during the two crises periods. Furthermore, the causal relationship between Bitcoin price clustering and the key variables varies depending on market conditions and the state of each variable. These findings are particularly valuable for decision-making and financial analysts, as they enhance the ability to predict cryptocurrency price clustering using available market data.

Suggested Citation

  • Fatma Hachicha & Nada Suissi & Amine Lahiani, 2025. "Bayesian Network in Machine Learning: An Empirical Investigation to Assess the Price Clustering Model During Crises," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 4897-4922, December.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:6:d:10.1007_s10614-025-10866-8
    DOI: 10.1007/s10614-025-10866-8
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