IDEAS home Printed from https://ideas.repec.org/a/hde/epregl/v72y2021i5p643-669.html
   My bibliography  Save this article

Predicting Bankruptcy Based On The Full Population Of Croatian Companies

Author

Listed:
  • SiniÅ¡a Bogdan

    (University of Rijeka, Faculty of Turism and Hospitality Management)

  • Luka Å ikić

    (University of Zagreb, Institute of Social Sciences Ivo Pilar)

  • Suzana BareÅ¡a

    (University of Rijeka, Faculty of Turism and Hospitality Management)

Abstract

This paper analyses the bankruptcy prediction based on the population of companies representative of the total business sector in Croatia. The representativity of the sample is achieved through the propensity score matching of the full population of bankrupt and similar non-bankrupt companies. The robust estimation of bankruptcy prediction is carried out through the multiple discriminant analysis (MDA) and logistic regression (logit). The results indicate high classification accuracy of both models, but more favourable performance of logit estimation. Overall accuracy of the MDA model was 73.7%, while the overall accuracy of the logit model was 76.3%. The results serve as a bankruptcy estimation benchmark for the business sector in Croatia.

Suggested Citation

  • SiniÅ¡a Bogdan & Luka Å ikić & Suzana BareÅ¡a, 2021. "Predicting Bankruptcy Based On The Full Population Of Croatian Companies," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), vol. 72(5), pages 643-669.
    • Handle: RePEc:hde:epregl:v:72:y:2021:i:5:p:643-669
    DOI: 10.32910/ep.72.5.1
    as

    Download full text from publisher

    File URL: https://doi.org/10.32910/ep.72.5.1
    Download Restriction: no
    File URL: https://libkey.io/10.32910/ep.72.5.1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dolinšek Tatjana & Kovač Tatjana, 2024. "Application of the Altman Model for the Prediction of Financial Distress in the Case of Slovenian Companies," Organizacija, Sciendo, vol. 57(2), pages 115-126, May.

    More about this item

    Keywords

    multiple discriminant analysis; MDA; logistic regression; logit; financial ratios;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

    Statistics

    Access and download statistics

    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:hde:epregl:v:72:y:2021:i:5:p:643-669. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Josip Tica (email available below). General contact details of provider: https://edirc.repec.org/data/hdeeeea.html .

    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.