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Bankruptcy prediction modelling in construction business
Burdych, Filip ; Kuběnka,, Michal (referee) ; Karas, Michal (advisor)
This master thesis deals with bankruptcy prediction models for construction companies doing business in Czech Republic. Terms important for understanding the issue are defined in the theoretical part. In analytical part, there are five current bankruptcy prediction models tested on the analysed sample and resulted accuracy compared with original ones. On the basis of knowledges acquired, there is developed a brand-new bankruptcy prediction model.
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Credit Risk Measurement in Manufacturing Industry Companies in the Czech Republic
Karas, Michal ; Dohnal, Mirko (referee) ; Hrvolová, Božena (referee) ; Myšková, Renáta (referee) ; Režňáková, Mária (advisor)
The purpose of this doctoral thesis is to create a new bankruptcy prediction model and also to design how to use this model for the purposes of credit risk measuring. The starting-point of this work is the analysis of traditional bankruptcy models. It was found out that the traditional bankruptcy model are not enough effective in the current economic conditions and it is necessary to create a new ones. Based on the identified deficiencies of the traditional models a set of two new model series was created. The first series of the created models is based on the use of parametric methods, and the second one is based on the use of newer nonparametric approach. Moreover, a set of factors which are able to identify an imminent bankruptcy was analyzed. It was found, that significant signs of imminent bankruptcy can be identified even five years before the bankruptcy occurs. Based on these findings a new model was created. This model incorporates variables of static and even dynamic character for bankruptcy prediction purposes. The overall classification accuracy of this model is 92.27% of correctly classified active companies and 95.65% of correctly classified bankrupt companies.
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Bankruptcy prediction modelling in construction business
Burdych, Filip ; Kuběnka,, Michal (referee) ; Karas, Michal (advisor)
This master thesis deals with bankruptcy prediction models for construction companies doing business in Czech Republic. Terms important for understanding the issue are defined in the theoretical part. In analytical part, there are five current bankruptcy prediction models tested on the analysed sample and resulted accuracy compared with original ones. On the basis of knowledges acquired, there is developed a brand-new bankruptcy prediction model.
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Credit Risk Measurement in Manufacturing Industry Companies in the Czech Republic
Karas, Michal ; Dohnal, Mirko (referee) ; Hrvolová, Božena (referee) ; Myšková, Renáta (referee) ; Režňáková, Mária (advisor)
The purpose of this doctoral thesis is to create a new bankruptcy prediction model and also to design how to use this model for the purposes of credit risk measuring. The starting-point of this work is the analysis of traditional bankruptcy models. It was found out that the traditional bankruptcy model are not enough effective in the current economic conditions and it is necessary to create a new ones. Based on the identified deficiencies of the traditional models a set of two new model series was created. The first series of the created models is based on the use of parametric methods, and the second one is based on the use of newer nonparametric approach. Moreover, a set of factors which are able to identify an imminent bankruptcy was analyzed. It was found, that significant signs of imminent bankruptcy can be identified even five years before the bankruptcy occurs. Based on these findings a new model was created. This model incorporates variables of static and even dynamic character for bankruptcy prediction purposes. The overall classification accuracy of this model is 92.27% of correctly classified active companies and 95.65% of correctly classified bankrupt companies.
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Comparisons of discriminant analysis and classification trees
Dlabač, Jaroslav ; Vilikus, Ondřej (advisor) ; Stecenková, Marina (referee)
This bachelor thesis compares two methods to discrimination and classification of data in multivariate statistics analysis. While discriminant analysis represents the classical statistical method for discrimination and subsequent classification data method, CART is a new procedure in data-minig, which uses artificial intelligence. The first half of this work is devoted to theoretical description and comparison of these two methods. The second half is the demonstration of both methods on practical example. At the end, the results of both methods are compared and evaluated.
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