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What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

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  • Keijo Kohv

    (School of Economics and Business Administration, University of Tartu, 51009 Tartu, Estonia)

  • Oliver Lukason

    (School of Economics and Business Administration, University of Tartu, 51009 Tartu, Estonia)

Abstract

This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process.

Suggested Citation

  • Keijo Kohv & Oliver Lukason, 2021. "What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains," Risks, MDPI, vol. 9(2), pages 1-19, January.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:2:p:29-:d:486527
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    References listed on IDEAS

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    Cited by:

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    2. Elżbieta Izabela Szczepankiewicz & Windham Eugene Loopesko & Farid Ullah, 2022. "A Model of Risk Information Disclosures in Non-Financial Corporate Reports of Socially Responsible Energy Companies in Poland," Energies, MDPI, vol. 15(7), pages 1-34, April.
    3. Elżbieta Izabela Szczepankiewicz, 2021. "Identification of Going-Concern Risks in CSR and Integrated Reports of Polish Companies from the Construction and Property Development Sector," Risks, MDPI, vol. 9(5), pages 1-31, May.
    4. Kui Wang & Jie Wan & Gang Li & Hao Sun, 2022. "A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry," Energies, MDPI, vol. 15(14), pages 1-18, July.

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