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

Fatma Hachicha (), Nada Suissi () and Amine Lahiani ()
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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

Computational Economics, 2025, vol. 66, issue 6, No 15, 4897-4922

Abstract: 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.

Keywords: Bayesian networks; Sensitivity; Inference analysis; Bitcoin price clustering; Investor sentiment; Geopolitical risk (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-025-10866-8

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