Which financial indicators do stakeholders have to focus on to manage risk of bank failure?

Zinaba Atman and Eric Lamarque ()
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Zinaba Atman: LAB IAE Paris - Sorbonne - IAE Paris - Sorbonne Business School
Eric Lamarque: LAB IAE Paris - Sorbonne - IAE Paris - Sorbonne Business School

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Abstract: Purpose This study aims to examine the informational content of financial disclosures from banks to identify the determinants that enable stakeholders and regulators to prevent the risk of bank failure. Design/methodology/approach This study is based on European bank database from 2010 to 2021. The authors classified the variables into groups based on their nature. Z -score return on assets (ROA) and Z -score return on risk-weighted assets (RORWA) were used to test these determinants. This study used the least absolute shrinkage and selection operator and applied the approaches proposed by Ahrens et al. (2020). The selected indicators were analysed using the theoretical framework of positive and normative accounting theories. Findings A selection of indicators from four groups of variables stands out because of their high coefficients in all samples: financial instruments, income statement impact, loan quality and macroeconomic variables. The selected groups of indicators are similar for all banks. However, the indicators selected to identify banking distress vary according to bank category (global systemically important banks (G-SIBs), domestic systemically important banks (D-SIBs) and financial conglomerates (FICO)). This study shows the importance of investment portfolio risk management and its impact on bank performance. Secondly, it highlights the informative value of the loan loss provisioning indicator and the default rate introduced by International Financial Reporting Standards 9. Finally, this study discusses the role of financial information and accounting standards theory as a decision-making tool for future activities. Originality/value This study is among the few to study the impact of financial distress in Europe during the study period using big data from Pillar 3 bank reports and machine learning methods.

Date: 2025
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Published in Review of Accounting and Finance, 2025

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05085094

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