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Classification Models as Tools of Bankruptcy Prediction—Polish Experience

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Classification, (Big) Data Analysis and Statistical Learning

Abstract

The purpose of this paper is to present the results of extensive empirical research conducted in Poland on the capability of the bankruptcy prediction models most commonly used in practice to provide a correct classification. A main dataset and its variants, forming the basis for the conducted studies, were described. A comparative analysis of the most frequent bankruptcy prediction models was performed, based on the example of manufacturing companies in Poland. Empirical and theoretical distributions of financial ratios which are diagnostic variables in the presented models were examined. In summary, the conclusions were formulated allowing us to provide an answer to four basic questions raised in the introduction to the paper.

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Notes

  1. 1.

    A part of the results presented here, concerning the application of the paring method and the random sampling with replacement method, were presented in a different paper by [2].

References

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  2. Baryła M., Pawełek B., Pociecha J.: Selection of Balanced Structure Samples in Corporate Bankruptcy Prediction. In: Wilhelm, A.F.X., Kestler,H.A. (eds.) Analysis of Large and Complex Data, Springer, pp. 345–355 (2016)

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  3. Bellovary, J., Giacomino, D., Akers, M.: A review of bankruptcy prediction studies: 1930 to present. J. Financial Educ. 33, 1–42 (2007)

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  4. McKee, T.E.: Developing a bankruptcy prediction model via rough sets theory. Int. J. Intell. Syst. Acc. Fin. Manag. 9(3), 159–173 (2000)

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Acknowledgements

The publication was financed from the funds granted to the Faculty of Management at Cracow University of Economics, within the framework of the subsidy for the maintenance of research potential.

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Correspondence to Józef Pociecha .

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Pociecha, J., Pawełek, B., Baryła, M., Augustyn, S. (2018). Classification Models as Tools of Bankruptcy Prediction—Polish Experience. In: Mola, F., Conversano, C., Vichi, M. (eds) Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55708-3_18

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