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Cure events in default prediction

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  • Wolter, Marcus
  • Rösch, Daniel

Abstract

This paper evaluates the resurrection event regarding defaulted firms and incorporates observable cure events in the default prediction of SME. Due to the additional cure-related observable data, a completely new information set is applied to predict individual default and cure events. This is a new approach in credit risk that, to our knowledge, has not been followed yet. Different firm-specific and macroeconomic default and cure-event-influencing risk drivers are identified. The significant variables allow a firm-specific default risk evaluation combined with an individual risk reducing cure probability. The identification and incorporation of cure-relevant factors in the default risk framework enable lenders to support the complete resurrection of a firm in the case of its default and hence reduce the default risk itself. The estimations are developed with a database that contains 5930 mostly small and medium-sized German firms and a total of more than 23000 financial statements over a time horizon from January 2002 to December 2007. Due to the significant influence on the default risk probability as well as the bank’s possible profit prospects concerning a cured firm, it seems essential for risk management to incorporate the additional cure information into credit risk evaluation.

Suggested Citation

  • Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:3:p:846-857
    DOI: 10.1016/j.ejor.2014.04.046
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    2. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
    3. Yuting Li & Tong Chen & Baogui Xin, 2016. "Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products," Sustainability, MDPI, vol. 8(5), pages 1-17, April.
    4. Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
    5. Wanying Song & Jian Min & Jianbo Yang, 2023. "Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    6. Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    7. Jianjun Yu & Dan Zhu, 2018. "Study on the Selection Strategy of Supply Chain Financing Modes Based on the Retailer’s Trade Grade," Sustainability, MDPI, vol. 10(9), pages 1-12, August.
    8. Lohmann, Christian & Ohliger, Thorsten, 2024. "Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability," Research in International Business and Finance, Elsevier, vol. 70(PB).

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