Predicting bankruptcy of manufacturing companies in EU

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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.identifier.issn 1212-3609
dc.identifier.uri https://dspace.tul.cz/handle/15240/22793
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.language.iso en
dc.publisher Technická Univerzita v Liberci cs
dc.publisher Technical university of Liberec, Czech Republic en
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
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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
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2018-1-011
dc.identifier.eissn 2336-5604
local.relation.volume 21
local.relation.issue 1
local.relation.abbreviation E+M cs
local.relation.abbreviation E&M en
local.faculty Faculty of Economics
local.citation.spage 159
local.citation.epage 174
local.access open
local.fulltext yes
local.filename EM_1_2018_11


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