Predicting Bankruptcy of Manufacturing Companies in EU

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Bankruptcy of Manufacturing Companies in EU

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting Bankruptcy of Manufacturing Companies in EU

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

E+M: Ekonomie a Management

ISSN

1212-3609

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

159-174

Kód UT WoS článku

000429786100011

EID výsledku v databázi Scopus

2-s2.0-85045069198