Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods


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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability


ISSN: 1234-7655
eISSN: 2450-0291





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VOLUME 22 , ISSUE 1 (March 2021) > List of articles

Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods

Aneta Ptak-Chmielewska *

Keywords : discriminant analysis, support vector machines, bankruptcy prediction, SMEs

Citation Information : Statistics in Transition New Series. Volume 22, Issue 1, Pages 179-195, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 23-December-2019 / Accepted: 17-February-2021 / Published Online: 03-March-2021



The impact the last financial crisis had on the small- and medium-sized enterprises (SMEs) sector varied across countries, affecting them on different levels and to a different extent. The economic situation in Poland during and after the financial crisis was quite stable compared to other EU member states. SMEs represent one of the most important segments of the economy of every country. Therefore, it is crucial to develop a prediction model which easily adapts to the characteristics of SMEs. Since the Altman Z-Score model was devised, numerous studies on bankruptcy prediction have been written. Most of them involve the application of traditional methods, including linear discriminant analysis (LDA), logistic regression and probit analysis. However, most recent studies in the area of bankruptcy prediction focus on more advanced methods, such as case-based reasoning, genetic algorithms and neural networks. In this paper, the effectiveness of LDA and SVM predictions were compared. A sample of SMEs was used in the empirical analysis, financial ratios were utilised and non-financial factors were taken account of. The hypothesis assuming that multidimensional discrimination was more effective was verified on the basis of the obtained results.

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