An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Abstract

This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers.

The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh

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“…After selecting the model training and testing, all model performances are measured. This study found that the ANNC is the best model for financial solvency prediction among all model, EC also has consistent results (Heo, 2014;Kim and Kang, 2010;Pisula, 2020;Ioannidis et al, 2010;Kim and Kang, 2010;Yeh et al, 2010;Olson et al, 2012).…”

“…Nevertheless, the logLoss value EC outperformed all other models as its value is close to zero. Many researchers also found consistent results by applying EC results (Heo, 2014; Kim and Kang, 2010; Pisula, 2020). Also, Figure 6 of ROC curves indicates the nearest neighbor classifier has a lower AUC.…”

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