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The Role of Financial, Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction

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  • Alnoor Bhimani
  • Mohamed Azzim Gulamhussen
  • Samuel da Rocha Lopes

Abstract

We assess the use of bank loan information in predicting the timing to default. We use unique data on defaults in small and medium enterprises maintained by the Central Bank of Portugal which includes financial accounting and macroeconomic indicators, as well as non-financial information. The findings are indicative of the incremental predictive ability of non-financial information over and above macroeconomic and financial accounting information in the baseline, industry, and in- and out-of-sample models. Specifically, total credit secured by firms is, as expected, negatively and significantly related to default. Gross domestic product is negatively and significantly related to default, and benchmark market rate is positively and significantly associated with default. The findings also reveal that firms which are operated by partners, which have stronger financial support from partners, and which possess operational assets exhibit lower hazards of default. The study indicates that non-financial information and macroeconomic indicators assessed alongside financial accounting data can significantly improve the forecasting performance of default models.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:euract:v:22:y:2013:i:4:p:739-763
    DOI: 10.1080/09638180.2013.770967
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    Cited by:

    1. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    2. Pranith Kumar Roy & Krishnendu Shaw & Alessio Ishizaka, 2023. "Developing an integrated fuzzy credit rating system for SMEs using fuzzy-BWM and fuzzy-TOPSIS-Sort-C," Annals of Operations Research, Springer, vol. 325(2), pages 1197-1229, June.
    3. Jianfei Shen & Lincong Han, 2020. "RETRACTED ARTICLE: Design process optimization and profit calculation module development simulation analysis of financial accounting information system based on particle swarm optimization (PSO)," Information Systems and e-Business Management, Springer, vol. 18(4), pages 809-822, December.
    4. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2023. "Building a core rule-based decision tree to explain the causes of insolvency in small and medium-sized enterprises more easily," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    5. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    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. Egor O. Bukharin & Sofia I. Mangileva & Vladislav V. Afanasev, 2024. "Default Prediction for Russian Food Service Firms: Contribution of Non-Financial Factors and Machine Learning," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(1), pages 206-226.
    8. Anagnostopoulou, Seraina C. & Drakos, Konstantinos, 2016. "Bank loan terms and conditions: Is there a macro effect?," Research in International Business and Finance, Elsevier, vol. 37(C), pages 269-282.
    9. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.

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