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Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy

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  • Pawełek Barbara

    (Department of Statistics, Cracow University of Economics, Kraków, Poland .)

Abstract

Machine learning methods are increasingly being used to predict company bankruptcy. Comparative studies carried out on selected methods to determine their suitability for predicting company bankruptcy have demonstrated high levels of prediction accuracy for the extreme gradient boosting method in this area. This method is resistant to outliers and relieves the researcher from the burden of having to provide missing data. The aim of this study is to assess how the elimination of outliers from data sets affects the accuracy of the extreme gradient boosting method in predicting company bankruptcy. The added value of this study is demonstrated by the application of the extreme gradient boosting method in bankruptcy prediction based on data free from the outliers reported for companies which continue to operate as a going concern. The research was conducted using 64 financial ratios for the companies operating in the industrial processing sector in Poland. The research results indicate that it is possible to increase the detection rate for bankrupt companies by eliminating the outliers reported for companies which continue to operate as a going concern from data sets.

Suggested Citation

  • Pawełek Barbara, 2019. "Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy," Statistics in Transition New Series, Statistics Poland, vol. 20(2), pages 155-171, June.
    • Handle: RePEc:vrs:stintr:v:20:y:2019:i:2:p:155-171:n:4
    DOI: 10.21307/stattrans-2019-020
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    References listed on IDEAS

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    1. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    2. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
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