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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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An Impact of Financial Crisis on Finance of Company
Farkaš, Timotej ; Mackerle, Mojmír (referee) ; Rejnuš, Oldřich (advisor)
The bachelor thesis is concerning of an impact financial crisis on finance of company, especially on loaning. In teoretical part there are basic knowledge of loans and bank rates. Progress of bank rates and loans in Czech Republic is analysed in practical part. Result of the bachelor thesis is interpretation of an impact financial crisis on finance of company.
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Rating Model for the Internal Assessment of the Creditworthiness of Customers
Vaňková, Leona ; Kotěrová, Monika (referee) ; Kocmanová, Alena (advisor)
The object of the following Diploma thesis was to design and test a simple and well-arranged credit tool – a Rating Model (further Model) which could be used as a preventative measure in credit policy. This rating model was designed in order to make the work of the Credit Risk Management sub-department and Rating Committee of KORADO more effective and at the same time to make more effective use of currently available internal and external data. Should the Company decide to use this Rating Model, a minimum of additional expenses would be incurred. The reader of this work will gain theoretical knowledge regarding financial analysis, credit management and fuzzy logic which is used in the calculation of a Total Rating. He/she will become acquainted with the major external agencies providing rating evaluations. As well, the reader may apprise the theoretical Rating model, including its practical use on four domestic and four overseas customers of KORADO. In conclusion there is an implementation of the Model, including a time schedule, an implementation team proposal as well as a budget of implementation and utilization costs.
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Binning numerical variables in credit risk models
Mattanelli, Matyáš ; Baruník, Jozef (advisor) ; Teplý, Petr (referee)
This thesis investigates the effect of binning numerical variables on the per- formance of credit risk models. The differences are evaluated utilizing five publicly available data sets, six evaluation metrics, and a rigorous statistical test. The results suggest that the binning transformation has a positive and significant effect on the performance of logistic regression, feedforward artifi- cial neural network, and the Naïve Bayes classifier. The most affected aspect of model performance appears to be its ability to differentiate between eligible and ineligible customers. The obtained evidence is particularly pronounced for moderately-sized data sets. In addition, the findings are robust to the inclusion of missing values, the elimination of outliers, and the exclusion of categorical features. No significant positive effect of the binning transformation was found for the decision tree algorithm and the Random Forest model.
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Energy Performance Certificate as One of Mortgage Default Determinants in the Czech Republic
Strašlipka, Jakub ; Pečená, Magda (advisor) ; Švéda, Josef (referee)
This thesis examines effects of buildings' energy efficiency, approximated by energy performance certificates, on default probabilities of mortgages in the Czech Republic. Data for the analysis is provided by a large Czech bank - cleaned sample contains information on 125 641 loans and is split into three groups based on collateral energy performance. Logit regression is employed for calculation of default probabilities and variations in conventional default predic- tors are controlled for. It is shown that mortgages on properties with certificate classes A, B have lower probabilities of default than those with certificate classes C, D by 7.1 bp (about 40%), ceteris paribus, whereas no statistical difference is found between default probabilities of mortgages on properties with classes C, D and those with classes E, F, G. The results can be considered by local banks in creation of climate risk management frameworks or in assessments of policies' impacts, or by other stakeholders in managing their expectations from banks. Due to partial use of the bank's internally estimated certificate proxies in the analysis and inconclusive results in a subsample of official certificates, follow-up research to confirm this thesis' findings is recommended.
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Use of fuzzy logic for banking risk assessment
Maňková, Veronika ; Bobalová, Martina (referee) ; Janková, Zuzana (advisor)
The thesis focuses on the use of fuzzy logic in the banking sector for credit risk assessment. Artificial intelligence, especially the fuzzy logic method, is used to construct a model for evaluating whether a client poses a threat to the bank or not based on their creditworthiness. The determination of a client's creditworthiness involves the evaluation of both financial and non-financial indicators. The paper further describes the use of the MATLAB tool for the development and implementation of this model. The resulting model allows banks to better understand credit risk and enables them to make more informed lending decisions.
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Receivables Management and Credit Risk in a Selected Company
PETŘÍKOVÁ, Markéta
The master's thesis is focused on the issue of receivables management and credit risk. The aim is to describe and evaluate the selected company's business strategy to receivables and credit risk management. Furthermore, the aim is to propose measures to improve this strategy of the company. The thesis is divided into a theoretical and a practical part. The analysis of the company is focused on the company's strategy in providing business loans, evaluating and monitoring the purchasers, setting their business conditions and solving problem debts. In this thesis, the current strategy of the company is evaluated, and new measures are proposed. The recommended improvements will increase the number of customers and the competitive ability of the analysed company. In the main part of the thesis, it is proposed to group together the customers of the company that share the same or similar characteristics using a cluster analysis. The implementation of such analysis can help the company to better identify its customers and consequently to provide their customers with more suitable business and credit conditions. Furthermore, a system for debt collection is proposed and an investment in a supplementary instrument to the existing accounting system is recommended. The cost of the investment is compared with the cost of the time spent generating and sending reminders. The recommended improvements will increase the number of customers and the competitive ability of the analysed company.
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Impact of COVID-19 fiscal measures on Non-Performing Loans
Bajcár, Tomáš ; Jakubík, Petr (advisor) ; Fanta, Nicolas (referee)
We study to which extent fiscal measures related to COVID-19 have mitigated credit risk proxied by non-performing loans (NPLs) in selected European countries. In this respect, we control for the macroeconomic and bank-specific determinants of non-performing loans. We limit our empirical analysis to NPLs and fiscal measures that aimed at non-financial corporations. We utilize a quarterly panel dataset covering the period from 2019 to 2021. We further employ split according to sectors of economic activity and cover 423 sectors in 23 European countries. The difference GMM estimation for dynamic panel data is utilized. Our empirical analysis suggests that the following variables significantly affect NPL ratios: economic growth, employment, nominal effective exchange rate and return on equity. In the case of the fiscal measures, public guarantees and tax reliefs were found to have a statistically significant and negative effect on NPLs. This finding supports the notion that during the COVID-19 pandemic, loan guarantees and lower tax burdens helped businesses maintain liquidity and solvency, which resulted in reduction of NPL ratios. Contrary, loan moratoria were found to positively affect NPL ratios. There is mixed evidence regarding direct grants and no empirical evidence was found in the case of...
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