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A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market

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  • Frieda Rikkers

    (Tilburg University, Netherlands)

  • Andre E. Thibeault

    (Vlerick Leuven Gent Management School, Belgium)

Abstract

The objective of this research is to develop a structural form probability of default model for small and medium-sized enterprises, dealing with the methodological issues which arise in the modelling of small commercial loan portfolios. Other motivations are to provide an extensive overview of the characteristics of SMEs, and to provide a list of characteristics for an SME PD model, e.g. time and cost efficiency, broad applicability, limited data requirements, and powerful in predicting default. The structural form model is developed and tested on a unique dataset of private firm’s bank loans of a Dutch bank. The results are promising; the model output differs significantly between defaulted and non-defaulted firms. The structural form model can be used on its own, or as an additional variable in a credit risk model. A second PD model is developed using logistic regression with a number of financial ratios, including the structural form measure. This variable is significant in default prediction of SMEs and has some additional predictive power, next to the popular financial ratios. Overall, the results indicate that the structural form model is a good indicator for default of SMEs.

Suggested Citation

  • Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    • Handle: RePEc:mfj:journl:v:13:y:2009:i:3-4:p:229-264
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    References listed on IDEAS

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    Cited by:

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    2. Stefano Filomeni & Udichibarna Bose & Anastasios Megaritis & Athanasios Triantafyllou, 2024. "Can market information outperform hard and soft information in predicting corporate defaults?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3567-3592, July.
    3. 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.
    4. Selcuk Caner & Mehmet Baha Karan, 2012. "Screening Creditworthiness of SME's: The Case of Small Business Assistance in Turkey," Multinational Finance Journal, Multinational Finance Journal, vol. 16(1-2), pages 1-20, March - J.
    5. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    6. 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.

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    More about this item

    Keywords

    SME; probability of default; structural form credit risk model; Basel II;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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