What drives the profitability of banking sectors in the European Union? The machine learning approach

Abstract The study aims to establish patterns of relations between the profitability of the European Union (EU) banking sectors between 2007 and 2021 and sets of variables appropriate for clusters of countries into which the 27 countries of the EU are divided. The random forest method is deployed to identify the factors influencing the value of the return on equity. Shapley additive explanations are exploited to add interpretability to the results. The results show that the sets of variables shaping the profitability of banking sectors in the EU grouped by use of sovereign rating criterion are different. However, there are variables common to all banking sectors. These include cost efficiency and default risk. The study’s novelty lies in the reliance on a broad spectrum of explanatory variables assigned to three groups of factors, reference to all EU countries, and decomposition of the sample to identify similarities among the determinants of profitability.

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Publication Details

Journal
International Journal of Management and Economics
Published
2024-09-20
DOI
https://doi.org/10.2478/ijme-2024-0022
Citations
2
Primary Topic
Banking stability, regulation, efficiency
Type
article
Field-Weighted Citation Impact
1.08

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article

What drives the profitability of banking sectors in the European Union? The machine learning approach

Paweł Niedziółka, Zbigniew Korzeb, Michał Bernardelli
2 citations
International Journal of Management and Economics
Banking stability, regulation, efficiency
1.08
article

What drives the profitability of banking sectors in the European Union? The machine learning approach

Paweł Niedziółka, Zbigniew Korzeb, Michał Bernardelli
article en
2 citations

Abstract

Abstract The study aims to establish patterns of relations between the profitability of the European Union (EU) banking sectors between 2007 and 2021 and sets of variables appropriate for clusters of countries into which the 27 countries of the EU are divided. The random forest method is deployed to identify the factors influencing the value of the return on equity. Shapley additive explanations are exploited to add interpretability to the results. The results show that the sets of variables shaping the profitability of banking sectors in the EU grouped by use of sovereign rating criterion are different. However, there are variables common to all banking sectors. These include cost efficiency and default risk. The study’s novelty lies in the reliance on a broad spectrum of explanatory variables assigned to three groups of factors, reference to all EU countries, and decomposition of the sample to identify similarities among the determinants of profitability.

International Journal of Management and EconomicsVol. 60(4)
Bialystok University of Technology (PL), SGH Warsaw School of Economics (PL)
Narodowa Agencja Wymiany Akademickiej
Partnerships for the goals
Openalex Percentile: Top 21%
Banking stability, regulation, efficiency
1.08
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What drives the profitability of banking sectors in the European Union? The machine learning approach — Paweł Niedziółka, Zbigniew Korzeb, et al. · International Journal of Management and Economics (2024) | TGRS Research Map | TGRS