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Predicting French SME failures: new evidence from machine learning techniques

Author

Listed:
  • Christophe Schalck
  • Meryem Yankol-Schalck

    (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)

Abstract

No abstract is available for this item.

Suggested Citation

  • Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Post-Print hal-03573319, HAL.
    • Handle: RePEc:hal:journl:hal-03573319
    DOI: 10.1080/00036846.2021.1934389
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    Cited by:

    1. Dina Ait Lahcen, 2023. "Synthetic Reading Of The Different Approaches And Models For Assessing The Risk Of Business Failure [Lecture Synthétique Des Diverses Approches Et Modèles D'Évaluation Du Risque De La Défaillance D," Post-Print hal-04009420, HAL.
    2. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

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