PREDICTING BANKRUPTCY OF MANUFACTURING COMPANIES IN EU
The best defense against existential problems of a company appears to be good ﬁnancial health, based on a satisfactory ﬁnancial situation. If, however, considerably weakened, the company gets into ﬁnancial distress, which may turn into a ﬁnancial crisis and end up in bankruptcy. The primary means, by which we would be informed about the condition of the enterprise, are bookkeeping data of a company. From there, based on ﬁnancial analysis, we can identify scenarios leading to good management decisions and, consequently, to the ﬁnancial health of the company.
Jméno a příjmení autora:
Václav Klepáč, David Hampel
Bankruptcy prediction, classiﬁcation, decision tree, feature selection
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Article focuses on the prediction of bankruptcy of the 1,000 medium-sized retail business companies in EU from which 170 companies gone bankrupt in 2014 with respect to lag of the used features. In…více
Article focuses on the prediction of bankruptcy of the 1,000 medium-sized retail business companies in EU from which 170 companies gone bankrupt in 2014 with respect to lag of the used features. In recent times, bankruptcy of manufacturing companies rapidly increased due to the impact of the recession, which produces economic and social problems accordingly. Therefore, the need for bankruptcy prediction models is very high. From various types of classiﬁcation models we chose Support vector machines method with spline, hyperbolic tangent and RBF ANOVA kernels, Decision trees, Random forests and Adaptive boosting to acquire best results. Pre-processing is enhanced with ﬁlter based feature selection like Gain ratio and Relief algorithm to acquire attributes with the best information value. As we can see both ﬁltering methods offers different variables to be used in the classiﬁcation and Decision trees wrapper algorithm chose less number than its competitors. Suitable attributes as ROA, Interest cover, Solvency ratio based on assets and Operating revenues were mostly used but it also changes across the time, which are probably very obtainable. It is apparent that inappropriate theoretical value of one variable does not necessarily lead to bankruptcy, so it is better to use combinations of these variables. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible ﬁnancial data offers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance of misclassiﬁcation rates. The Random forest and Decision trees offer better accuracy for bankruptcy prediction than SVM method, both method offers prediction accuracy which is comparable to previous empirical studies.