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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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    1. Giulio Bottazzi & Marco Grazzi & Angelo Secchi & Federico Tamagni, 2011. "Financial and economic determinants of firm default," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 373-406, August.
    2. Erkki K. Laitinen & Oliver Lukason, 2014. "Do firm failure processes differ across countries: evidence from Finland and Estonia," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(5), pages 810-832, November.
    3. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    4. Lukason, Oliver & Laitinen, Erkki K., 2019. "Firm failure processes and components of failure risk: An analysis of European bankrupt firms," Journal of Business Research, Elsevier, vol. 98(C), pages 380-390.
    5. Giampaolo Gabbi & Michele Giammarino & Massimo Matthias, 2020. "Die Hard: Probability of Default and Soft Information," Risks, MDPI, vol. 8(2), pages 1-12, May.
    6. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    9. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    10. Virgo Süsi & Oliver Lukason, 2019. "Corporate governance and failure risk: evidence from Estonian SME population," Management Research Review, Emerald Group Publishing Limited, vol. 42(6), pages 703-720, February.
    11. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    12. Oliver Lukason & Art Andresson, 2019. "Tax Arrears Versus Financial Ratios in Bankruptcy Prediction," JRFM, MDPI, vol. 12(4), pages 1-13, December.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    14. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    15. Alnoor Bhimani & Mohamed Azzim Gulamhussen & Samuel da Rocha Lopes, 2013. "The Role of Financial, Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction," European Accounting Review, Taylor & Francis Journals, vol. 22(4), pages 739-763, December.
    16. Raffaella Calabrese & Silvia Angela Osmetti, 2013. "Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1172-1188, June.
    17. Fergal McCann & Tara McIndoe-Calder, 2015. "Firm size, credit scoring accuracy and banks' production of soft information," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3594-3611, July.
    18. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    19. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    20. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel Da-Rocha, 2010. "Accounting and non-accounting determinants of default: An analysis of privately-held firms," Journal of Accounting and Public Policy, Elsevier, vol. 29(6), pages 517-532, November.
    21. Peter Back, 2005. "Explaining financial difficulties based on previous payment behavior, management background variables and financial ratios," European Accounting Review, Taylor & Francis Journals, vol. 14(4), pages 839-868.
    22. Edward I. Altman & Małgorzata Iwanicz-Drozdowska & Erkki K. Laitinen & Arto Suvas, 2020. "A Race for Long Horizon Bankruptcy Prediction," Applied Economics, Taylor & Francis Journals, vol. 52(37), pages 4092-4111, July.
    23. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    24. Oliver Lukason & María-del-Mar Camacho-Miñano, 2019. "Bankruptcy Risk, Its Financial Determinants and Reporting Delays: Do Managers Have Anything to Hide?," Risks, MDPI, vol. 7(3), pages 1-15, July.
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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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