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A Race for Long Horizon Bankruptcy Prediction

Author

Listed:
  • Edward I. Altman
  • Małgorzata Iwanicz-Drozdowska
  • Erkki K. Laitinen
  • Arto Suvas

Abstract

This study compares the accuracy and efficiency of five different estimation methods for predicting financial distress of small and medium-sized enterprises. We apply different methods for a large set of financial and non-financial variables, using filter and wrapper selection, to predict bankruptcy up to 10 years before the event in an open, European economy. Our findings show that logistic regression and neural networks are superior to other approaches. We document how the cost-return ratio considerably affects the location of optimal cut-off points and attainable profit in credit decisions. Once a loan provider selects a particular prediction model, an effort should be made to find the optimal cut-off score to maximize the efficiency of the technique. Indeed, this often involves determining several cut-off levels where the portfolio of products and services exhibits different cost-return characteristics.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:37:p:4092-4111
    DOI: 10.1080/00036846.2020.1730762
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    Citations

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    Cited by:

    1. Sarbjit Singh Oberoi & Sayan Banerjee, 2023. "Bankruptcy Prediction of Indian Banks Using Advanced Analytics," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 22-41.
    2. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    3. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    4. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    5. 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.
    6. 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.
    7. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
    8. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
    9. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
    10. Dariusz Sala & Kostiantyn Pavlov & Olena Pavlova & Anton Demchuk & Liubomur Matiichuk & Dariusz Cichoń, 2023. "Determining of the Bankrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity," Energies, MDPI, vol. 16(4), pages 1-13, February.
    11. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    12. Oliver Lukason & Germo Valgenberg, 2021. "Failure Prediction in the Condition of Information Asymmetry: Tax Arrears as a Substitute When Financial Ratios Are Outdated," JRFM, MDPI, vol. 14(10), pages 1-13, October.
    13. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    14. Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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