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Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment

    Beata Gavurova Affiliation
    ; Miroslava Packova Affiliation
    ; Maria Misankova Affiliation
    ; Lubos Smrcka Affiliation

Abstract

In our study, we focused on the assessment of four bankruptcy prediction models, to figure out which model is most appropriate in the conditions of the Slovak business environment. Based on the previous research within the Slovak conditions, we set a portfolio of 4 models to be assessed: Altman model (1984), Ohlson model (1980), indexes IN01 and IN05 that were validated on the sample of 700 Slovak companies. Based on previous studies we expected that IN indexes are superior to Ohlson and Altman model. The excellency of our research lies in validation and assessing the accuracy of bankruptcy prediction models at three levels: the overall accuracy, accuracy of the bankruptcy prediction, and the non-bankruptcy prediction accuracy. This analytical structure enables to look at the topic more complexly and to increase the objectification of accuracy of analysed models. Based on the results, we showed that Ohlson model is not applicable to predict bankruptcy in the Slovak conditions as reached the lowest bankruptcy prediction ability even if has high non bankruptcy prediction ability. On the other hand, we have confirmed our expectation about the bankruptcy prediction ability of index IN05, that is proven to be superior to Ohlson and Altman model and so is the most appropriate model for Slovak business environment.

Keyword : bankruptcy, prediction models, bankruptcy and non-bankruptcy prediction accuracy, validation of prediction models, Altman model, Ohlson model, IN indices

How to Cite
Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156-1173. https://doi.org/10.3846/16111699.2017.1400461
Published in Issue
Dec 20, 2017
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