Extreme gradient boosting method in the prediction of company bankruptcy

• Authors: Barbara Pawełek

Abstracts

EN

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.

Keywords

EN

XGBoost; company bankruptcy; machine learning; outlier

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2019
• Volume: 20
• Issue: 2
• Pages: 155-171

Contributors

author

Barbara Pawełek

• Department of Statistics, Cracow University of Economics, Kraków, Poland

• https://orcid.org/0000000295896043

Identifiers

DOI

10.21307/stattrans-2019-020

Biblioteka Nauki

1194455