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Credit Risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium‐Sized Enterprises

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  • Patrick Behr
  • André Güttler

Abstract

We estimate a logit scoring model for the prediction of the probability of default by German small and medium‐sized enterprises (SMEs) using a unique data set on SME loans in Germany. Our scoring model helps SMEs to gain knowledge about their default risk, which can be used to approximate their risk adequate cost of debt. This knowledge is likely to lead to a detection of hold‐up problems that German SMEs might be confronted with in their bank relationships. Furthermore, it allows them to monitor their bank’s pricing behavior and it reduces information asymmetries between lenders and borrowers. Finally, it can influence their future financing decisions toward capital market‐based financing.

Suggested Citation

  • Patrick Behr & André Güttler, 2007. "Credit Risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium‐Sized Enterprises," Journal of Small Business Management, Taylor & Francis Journals, vol. 45(2), pages 194-213, April.
    • Handle: RePEc:taf:ujbmxx:v:45:y:2007:i:2:p:194-213
    DOI: 10.1111/j.1540-627X.2007.00209.x
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    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.

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