Bankruptcy prediction model for listed companies in Greece

  • Received February 23, 2021;
    Accepted May 20, 2021;
    Published May 27, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.18(2).2021.14
  • Article Info
    Volume 18 2021, Issue #2, pp. 166-180
  • TO CITE АНОТАЦІЯ
  • Cited by
    7 articles
  • 1186 Views
  • 719 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This paper deals with the ever-increasing issue of bankruptcy prediction in distressed economies. Specifically, the aim of this study is to create a model by establishing a new set of predictor variables, which achieves significant discrimination among listed manufacturing firms in Greece, by using multivariate discriminant analysis (MDA). An equally balanced matched sample of 28 Greek-listed manufacturing firms was used in this study covering the distressed period from 2008 to 2015 (including all firms that went bankrupt between 2008–2015). It is found that the quick ratio, cash flow interest coverage, and economic value added (EVA) divided by total assets are significant for predicting bankruptcy in Greece. The discriminant analysis (DA) model comprised the aforementioned variables and correctly classified 96.43% of grouped cases 1 year before bankruptcy. The adjusted DA prediction model for two and three years before bankruptcy used the same variables and correctly classified 92.86% and 89.29% of grouped cases, respectively. Consequently, this mix of financial ratios achieved strong classification accuracy even three years before bankruptcy, captivating an overall picture of a firm’s financial health and providing a powerful tool for decision making to investors and risk managers in the banking section and economic policy makers.

view full abstract hide full abstract
    • Table 1. Initial list of potential predictor variables
    • Table 2. Descriptive group statistics (t–1)
    • Table 3. Outline of canonical discriminant functions (t–1)
    • Table 4. Coefficients of the discriminant function (t–1)
    • Table 5. Group centroids (t–1)
    • Table 6. Classification results (t–1)
    • Table 7. Descriptive group statistics (t–2)
    • Table 8. Outline of canonical discriminant functions (t–2)
    • Table 9. Coefficients of the discriminant function (t–2)
    • Table 10. Group centroids (t–2)
    • Table 11. Classification results (t–2)
    • Table 12. Descriptive group statistics (t–3)
    • Table 13. Outline of canonical discriminant functions (t–3)
    • Table 14. Coefficients of the discriminant function (t–3)
    • Table 15. Group centroids (t–3)
    • Table 16. Classification results (t–3)
    • Conceptualization
      Evangelos Sfakianakis
    • Data curation
      Evangelos Sfakianakis
    • Formal Analysis
      Evangelos Sfakianakis
    • Funding acquisition
      Evangelos Sfakianakis
    • Investigation
      Evangelos Sfakianakis
    • Methodology
      Evangelos Sfakianakis
    • Project administration
      Evangelos Sfakianakis
    • Resources
      Evangelos Sfakianakis
    • Software
      Evangelos Sfakianakis
    • Supervision
      Evangelos Sfakianakis
    • Validation
      Evangelos Sfakianakis
    • Visualization
      Evangelos Sfakianakis
    • Writing – original draft
      Evangelos Sfakianakis
    • Writing – review & editing
      Evangelos Sfakianakis