Zobrazit minimální záznam

dc.contributor.authorStaňková, Michaela
dc.contributor.authorHampel, David
dc.date.accessioned2021-07-17T00:02:19Z
dc.date.available2021-07-17T00:02:19Z
dc.date.issued2018
dc.identifier43915827
dc.identifier.issn1211-8516 Sherpa/RoMEO, JCR
dc.identifier.urihttps://repozitar.mendelu.cz/xmlui/handle/20.500.12698/1374
dc.description.abstractThis article focuses on the problem of binary classification of 902 small- and medium-sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.en
dc.format1347-1356
dc.publisherMendelova univerzita v Brně
dc.relation.ispartofActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
dc.relation.urihttps://doi.org/10.11118/actaun201866051347
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBankruptcy predictionen
dc.subjectBinary classificationen
dc.subjectClassification treesen
dc.subjectLogistic regressionen
dc.subjectSupport vector machinesen
dc.titleBankruptcy prediction of engineering companies in the EU using classification methodsen
dc.typeJ_ČLÁNEK
dc.date.updated2021-07-17T00:02:19Z
dc.description.versionOA
local.identifier.doi10.11118/actaun201866051347
local.identifier.scopus2-s2.0-85056277770
local.number5
local.volume66
local.identifier.obd43915827
local.identifier.e-issn2464-8310
dc.project.IDPEF_DP_2017027
dc.project.IDHodnocení efektivity podniků ve vybraných zemích EU
dc.identifier.orcidStaňková, Michaela 0000-0003-3789-3570
dc.identifier.orcidHampel, David 0000-0002-3865-5948
local.contributor.affiliationPEF


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Zobrazit minimální záznam

CC BY-NC-ND 4.0
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