IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-00543111.html
   My bibliography  Save this paper

L'Enchaînement Des Facteurs De Défaillance De L'Entreprise : Une Réconciliation Des Approches Organisationnelles Et Financières

Author

Listed:
  • Nathalie Crutzen

    (Centre d'Étude de la Performance des Entreprises - HEC École de Gestion de l'Université de Liège)

  • Didier van Caillie

    (Centre d'Étude de la Performance des Entreprises - HEC École de Gestion de l'Université de Liège)

Abstract

L'objet de la présente contribution est de mettre en commun les enseignements issus des différentes recherches consacrées aux trajectoires de défaillances, afin d'aboutir à un modèle unificateur représentatif de l'enchaînement des facteurs de défaillance qui soit le plus complet et le plus objectif possible. Une analyse approfondie de la littérature existante et des manques à combler nous conduit à l'élaboration d'une grille de lecture qui permet de voir, d'une manière générale, les différentes étapes par lesquelles passe une entreprise lorsqu'elle évolue vers la faillite juridique, tout en mettant en évidence le fait que toutes les entreprises n'empruntent pas le même chemin avant de disparaître (Argenti, 1976).

Suggested Citation

  • Nathalie Crutzen & Didier van Caillie, 2007. "L'Enchaînement Des Facteurs De Défaillance De L'Entreprise : Une Réconciliation Des Approches Organisationnelles Et Financières," Post-Print halshs-00543111, HAL.
  • Handle: RePEc:hal:journl:halshs-00543111
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00543111
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-00543111/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    2. Sofie Balcaen & Sophie Manigart & Hubert Ooghe, 2011. "From distress to exit: determinants of the time to exit," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 407-446, August.
    3. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    4. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    5. Zopounidis, Constantin & Doumpos, Michael, 2001. "A preference disaggregation decision support system for financial classification problems," European Journal of Operational Research, Elsevier, vol. 130(2), pages 402-413, April.
    6. Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
    7. Michael Halling & Evelyn Hayden, 2008. "Bank failure prediction: a two-step survival time approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 56th ISI Session, Lisbon, August 2007, volume 28, pages 48-73, Bank for International Settlements.
    8. Saiki Tsuchiya & Shinichi Nishioka, 2014. "Estimation of Firms' Default Rates in terms of Intangible Assets," Bank of Japan Working Paper Series 14-E-2, Bank of Japan.
    9. Wijn, M.F.C.M. & Bijnen, E.J., 2001. "Firm size and bankruptcy elasticity," Research Memorandum FEW797, Tilburg University, School of Economics and Management.
    10. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    11. Fejér-Király Gergely, 2015. "Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 3(1), pages 93-108, December.
    12. Kallunki, J. -P. & Martikainen, T., 1999. "Financial failure and managers' accounting responses: Finnish evidence," Journal of Multinational Financial Management, Elsevier, vol. 9(1), pages 15-26, January.
    13. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    14. Govindan, Kannan & Jepsen, Martin Brandt, 2016. "ELECTRE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 250(1), pages 1-29.
    15. Doumpos, Michalis & Andriosopoulos, Kostas & Galariotis, Emilios & Makridou, Georgia & Zopounidis, Constantin, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," European Journal of Operational Research, Elsevier, vol. 262(1), pages 347-360.
    16. Peter Lloyd Jones, 2011. "The determinants of aggregate creditors' voluntary liquidations," Post-Print hal-00762895, HAL.
    17. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    18. Zopounidis, C., 1999. "Multicriteria decision aid in financial management," European Journal of Operational Research, Elsevier, vol. 119(2), pages 404-415, December.
    19. Bosiljka Srebro & Bojan Mavrenski & Vesna Bogojević Arsić & Snežana Knežević & Marko Milašinović & Jovan Travica, 2021. "Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    20. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:halshs-00543111. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.