Predicting Bankruptcy of Manufacturing Companies in EU

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43913435" target="_blank" >RIV/62156489:43110/18:43913435 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.15240/tul/001/2018-1-011" target="_blank" >https://doi.org/10.15240/tul/001/2018-1-011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15240/tul/001/2018-1-011" target="_blank" >10.15240/tul/001/2018-1-011</a>

Result language

angličtina

Original language name

Predicting Bankruptcy of Manufacturing Companies in EU

Original language description

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 classifi cation 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 fi lter based feature selection like Gain ratio and Relief algorithm to acquire attributes with the best information value. As we can see both fi ltering methods offers different variables to be used in the classifi cation 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 fi nancial data offers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance of misclassifi cation 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

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

E+M: Ekonomie a Management

ISSN

1212-3609

e-ISSN

—

Volume of the periodical

21

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

159-174

UT code for WoS article

000429786100011

EID of the result in the Scopus database

2-s2.0-85045069198