IDEAS home Printed from https://ideas.repec.org/a/pes/ieroec/v8y2017i1p143-155.html
   My bibliography  Save this article

International Comparison Of The Relevant Variables In The Chosen Bankruptcy Models Used In The Risk Management

Author

Listed:
  • Katarina Zvarikova

    (University of Zilina, Slovakia)

  • Erika Spuchlakova

    (University of Zilina, Slovakia)

  • Gabriela Sopkova

    (University of Economics in Bratislava, Slovakia)

Abstract

Research background: It does not matter if the company is operating in the domestic or in the international environment; its failure has serious impact on its environment. Because of this fact it is not surprising that not only owners of the companies, but also another interested groups are focused on the prediction of the company´s financial health. Purpose of the article: The first studies concerned with this issue are dating back to 1930 but from this time a hundreds of bankruptcy prediction models have been constructed all over the world. Some of them are known world-wide and some of them are known only on the national level. Many researchers share their opinion, that it is not appropriate to use foreign models in the domestic conditions non-critically, because they were constructed in the different conditions. One of the main problems are used variables. Methods: We mention three studies which were focused on the used variables in the bankruptcy prediction models. Our comparative study was concerning with 42 models constructed in the seven chosen transit economics with the aim to realize which variables are relevant and which could be reduce from the bankruptcy prediction models. We focused only on the used variables and abstracted from the used methodology, the date of their construction or the model´s power of relevancy. Findings & Value added: The result of our comparative study is the identification of 20 variables, which were used in three or more prediction models, so we assume that these variables have the best prediction ability in the condition of transit economics and their application should be consider in the construction of new models.

Suggested Citation

  • Katarina Zvarikova & Erika Spuchlakova & Gabriela Sopkova, 2017. "International Comparison Of The Relevant Variables In The Chosen Bankruptcy Models Used In The Risk Management," Oeconomia Copernicana, Institute of Economic Research, vol. 8(1), pages 145-157, March.
  • Handle: RePEc:pes:ieroec:v:8:y:2017:i:1:p:143-155
    DOI: 10.24136/oc.v8i1.10
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.24136/oc.v8i1.10
    Download Restriction: no

    File URL: https://libkey.io/10.24136/oc.v8i1.10?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. Nagy Marek & Valaskova Katarina, 2023. "An Analysis of the Financial Health of Companies Concerning the Business Environment of the V4 Countries," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 170-193, June.
    2. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    3. Svabova Lucia & Durica Marek & Podhorska Ivana, 2018. "Prediction of Default of Small Companies in the Slovak Republic," Economics and Culture, Sciendo, vol. 15(1), pages 88-95, June.
    4. Gajdosikova Dominika & Valaskova Katarina, 2022. "The Impact of Firm Size on Corporate Indebtedness: A Case Study of Slovak Enterprises," Folia Oeconomica Stetinensia, Sciendo, vol. 22(1), pages 63-84, June.
    5. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.

    More about this item

    Keywords

    company failure; bankruptcy prediction models; relevant variables; transit economy;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:pes:ieroec:v:8:y:2017:i:1:p:143-155. 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: Adam P. Balcerzak (email available below). General contact details of provider: https://edirc.repec.org/data/ibgtopl.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.