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Predicting default of a small business using different definitions of financial distress

Author

Listed:
  • S-M Lin

    (The University of Edinburgh, Edinburgh, UK)

  • J Ansell

    (The University of Edinburgh, Edinburgh, UK)

  • G Andreeva

    (The University of Edinburgh, Edinburgh, UK)

Abstract

The paper introduces a number of risk-rating models for UK small businesses applying an accounting-based approach, which uses financial ratios to predict corporate bankruptcy. An enhancement to these models is considered through features typical to retail credit risk modelling. A common problem of default prediction consists in the relatively small number of bankruptcies or real defaults available for model-building. In order to expand the ‘default’ group beyond bankrupt companies, the paper considers adopting four different definitions of ‘a failing business’ by investigating combinations of financial distress levels. The impact of each default definition on the choice of predictor variables and on the model's predictive accuracy is explored. In addition, the paper examines the value of categorizing financial ratios used as predictor variables.

Suggested Citation

  • S-M Lin & J Ansell & G Andreeva, 2012. "Predicting default of a small business using different definitions of financial distress," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(4), pages 539-548, April.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:4:p:539-548
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    Citations

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    Cited by:

    1. Gupta, Jairaj & Gregoriou, Andros, 2018. "Impact of market-based finance on SMEs failure," Economic Modelling, Elsevier, vol. 69(C), pages 13-25.
    2. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    3. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    4. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    5. Tingqiang Chen & Suyang Wang, 2023. "Incomplete information model of credit default of micro and small enterprises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2956-2974, July.
    6. Georgios Marinakos & Sophia Daskalaki & Theodoros Ntrinias, 2014. "Defensive financial decisions support for retailers in Greek pharmaceutical industry," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 525-551, September.
    7. Arvind Shrivastava & Kuldeep Kumar & Nitin Kumar, 2018. "Business Distress Prediction Using Bayesian Logistic Model for Indian Firms," Risks, MDPI, vol. 6(4), pages 1-15, October.
    8. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    9. 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.
    10. Xiaoting Wei & Cameron Truong & Viet Do, 2020. "When are dividend increases bad for corporate bonds?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1295-1326, June.
    11. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    12. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    13. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    14. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    15. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.

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