Predicting bankruptcy of manufacturing companies in EU
| dc.contributor.author | Klepáč, Václav | |
| dc.contributor.author | Hampel, David | |
| dc.contributor.other | Ekonomická fakulta | cs |
| dc.date.accessioned | 2018-03-29 | |
| dc.date.available | 2018-03-29 | |
| dc.date.issued | 2018-03-29 | |
| dc.description.abstract | 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. | en |
| dc.format | text | |
| dc.format.extent | 16 stran | cs |
| dc.identifier.doi | 10.15240/tul/001/2018-1-011 | |
| dc.identifier.eissn | 2336-5604 | |
| dc.identifier.issn | 1212-3609 | |
| dc.identifier.uri | https://dspace.tul.cz/handle/15240/22793 | |
| dc.language.iso | en | |
| dc.publisher | Technická Univerzita v Liberci | cs |
| dc.publisher | Technical university of Liberec, Czech Republic | en |
| dc.publisher.abbreviation | TUL | |
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| dc.relation.ispartof | Ekonomie a Management | cs |
| dc.relation.ispartof | Economics and Management | en |
| dc.relation.isrefereed | true | |
| dc.rights | CC BY-NC | |
| dc.subject | bankruptcy prediction | en |
| dc.subject | classification | en |
| dc.subject | decision tree | en |
| dc.subject | feature selection | en |
| dc.subject.classification | C52 | |
| dc.subject.classification | C44 | |
| dc.title | Predicting bankruptcy of manufacturing companies in EU | en |
| dc.type | Article | en |
| local.access | open | |
| local.citation.epage | 174 | |
| local.citation.spage | 159 | |
| local.faculty | Faculty of Economics | |
| local.filename | EM_1_2018_11 | |
| local.fulltext | yes | |
| local.relation.abbreviation | E+M | cs |
| local.relation.abbreviation | E&M | en |
| local.relation.issue | 1 | |
| local.relation.volume | 21 |
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