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General bankruptcy prediction models for the Visegrád Group. The stability over time

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  • Sebastian Klaudiusz Tomczak

Abstract

Managers of enterprises must constantly face the continual changes on the market and fight for survival in a world of high competition. Therefore, it is important to systematically monitor the company’s financial condition. This will help to identify problems and give specific time to take corrective action. Bankruptcy prediction models are usually constructed for local goals. The purpose of the article is to build not only regional but also general discriminant and logit models for the SMEs operating in the construction industry in Visegrád Group countries. A total of 32 unique models were built and verified along with the Altman model for emerging markets. The paper also contributes to the literature by assessing the stability of the constructed models over time, which the models’ authors do not usually measure. The results showed that regional models are characterized by higher accuracy than general ones. However, general models can be adapted to the analyzed Visegrád Group with an accuracy of approximately 90%. The G1 LR model can be considered the best model, as it has relatively high accuracy and over-time stability.

Suggested Citation

  • Sebastian Klaudiusz Tomczak, 2023. "General bankruptcy prediction models for the Visegrád Group. The stability over time," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(4), pages 171-187.
    • Handle: RePEc:wut:journl:v:33:y:2023:i:4:p:171-187:id:10
    DOI: 10.37190/ord2304010
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    References listed on IDEAS

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    1. Maria Kovacova & Tomas Kliestik & Katarina Valaskova & Pavol Durana & Zuzana Juhaszova, 2019. "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 10(4), pages 743-772, December.
    2. Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.
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