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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 Des Entreprises]

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  • Dina Ait Lahcen

    (UM5 - Université Mohammed V de Rabat [Agdal])

Abstract

The interest of this present research work is to try to study the academic corpus related to the subject of the failure of companies in order to be able to better apprehend it. The examination of the literature which resulted from it allowed us to raise and to study on the one hand, the existence of three types of approaches, namely the static, dynamic and processual approach of the failure of the companies and on the other hand the existence of several types of statistical and probabilistic models allowing to evaluate and to prevent at best the risk of the failure.

Suggested Citation

  • 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.
    • Handle: RePEc:hal:journl:hal-04009420
    DOI: 10.48375/IMIST.PRSM/remses-v7i3.34726
    Note: View the original document on HAL open archive server: https://hal.science/hal-04009420
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    References listed on IDEAS

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    1. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
    2. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    3. Michel Dietsch & Joël Petey, 2003. "Mesure et gestion du risque de crédit dans les institutions financières," ULB Institutional Repository 2013/14375, ULB -- Universite Libre de Bruxelles.
    4. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    5. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Post-Print hal-03573319, HAL.
    Full references (including those not matched with items on IDEAS)

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