Bankruptcy prediction of engineering companies in the EU using classification methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43914386" target="_blank" >RIV/62156489:43110/18:43914386 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.11118/actaun201866051347" target="_blank" >https://doi.org/10.11118/actaun201866051347</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201866051347" target="_blank" >10.11118/actaun201866051347</a>

Result language

angličtina

Original language name

Bankruptcy prediction of engineering companies in the EU using classification methods

Original language description

This 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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Volume of the periodical

66

Issue of the periodical within the volume

5

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

10

Pages from-to

1347-1356

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85056277770