National Repository of Grey Literature 4 records found  Search took 0.00 seconds. 
Scoring of the Firm’s Financial Risk
Dodek, Radim ; Kříž, Václav (referee) ; Režňáková, Mária (advisor)
This Bachelor diploma is focused on comparison of chosen scoring indicators and selecting those, which describes the real financial situation of the metallurgical companies. The output of the diploma is to compare particular. Visual Basic application has been developed to counting scoring indicators.
The decision on the introduction of external scoring model based on a comparison to the current internal solution
Hrubá, Elina ; Bína, Vladislav (advisor) ; Světlík, Jiří (referee)
In my thesis I have analyzed internal and external scoring model of financial organization. I have prepared comprehensive comparison and evaluation of both internal and external scoring systems. The aim of the thesis was creating a complete assessment of external scoring system with the simplified financial analysis and also with taking into the consideration appropriateness of this offer before approving purchase of external model.
Scoring of the Firm’s Financial Risk
Dodek, Radim ; Kříž, Václav (referee) ; Režňáková, Mária (advisor)
This Bachelor diploma is focused on comparison of chosen scoring indicators and selecting those, which describes the real financial situation of the metallurgical companies. The output of the diploma is to compare particular. Visual Basic application has been developed to counting scoring indicators.
Comparison of approaches to creating credit scoring models
Hofman, Elena ; Šedivý, Jan (advisor)
This work is focused on the management of a credit risk related to the traditional bank lending business to individuals. The paper deals with a theory of measuring risk with help of PD (Probability of Default) parameter when different scoring models are used. The goal is to outline an issue with the credit risk and its management in general, attention is paid to details of a process of creating scoring models. There are three specific modeling techniques listed, namely logistic regression, decision trees and neural networks. Methods are explained in detail and are given possibilities of mutual comparison. The application part is devoted to the evaluation and comparison of credit scoring models based on these methods.

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