Diplomová práce se zabývá aplikací fuzzy logiky na proces automatické detekce úpadkového klienta z pohledu řízení úvěrového rizika banky. Na základě analýzy stávajícího informačního systému Credit Risk Monitoring autor navrhuje změnu přístupu v hodnocení úvěrového klienta.

Cette thèse étudie le secteur bancaire tunisien et pourquoi ce dernier se caractérise par des volumesimportants de prêts non performants. Sur un échantillon de 11 banques commerciales cotées durant lapériode 2009-2011, nous examinons l’impact de la structure de propriété et des caractéristiques duconseil d’administration sur le risque de crédit. Nos résultats des régressions sur données de panelrévèlent que ces mécanismes de gouvernance sont défaillants jusqu’à présent et qu’ils ont contribué àl’aggravation des prêts improductifs. A partir de l’apparition de nouvelles tentatives à expliquer lesdéfaillances bancaires par l’apport de la finance comportementale, nous concluons le rôle de l’excès deconfiance dans la gestion de risque de crédit imprudente des banques tunisiennes.Dans le but d’approfondir notre recherche, il nous semble intéressant de vérifier l’impact de l’excès deconfiance sur le comportement de prise de risque d’un responsable de crédits. Les régressionslogistiques multinomiales montrent que l’excès de confiance chez les responsables de crédit évolueavec l’expérience et influence négativement leurs prises de risque de crédit. / This thesis studies the Tunisian banking sector and why it is characterized by large volumes of nonperformantloans. Based on a sample of 11 commercial listed banks during 2009-2011, we examinethe impact of ownership structure and board characteristics on credit risk. Our results reveal thedeficiency of these governance mechanisms.From the appearance of new attempts to explain bank failures by the contribution of behavioralfinance, we try to identify the role of overconfidence in the reckless credit policy. Our results showthat overconfidence recent worsening Non Performants Loans of Tunisian banks.In order to deepen our research, it seems interesting to check the impact of overconfidence on bankers’risk-taking behavior. We extend our work with an experimental study to detect the impact ofoverconfidence on the banker’s risk-taking behavior. Our results of multinomial logistic regressionsshow that banker’s overconfidence evolves with experience and influences negatively his credit risktakingbehavior.

Machine learning is becoming a part of everyday life and has an indisputable impact across large array of industries. In the financial industry, this impact lies particularly in predictive modelling. The goal of this thesis is to describe the basic principles of artificial intelligence and its subset, machine learning. The most widely used machine learning techniques are outlined both in a theoretical and a practical way. As a result, four models were assembled within the thesis. Results and limitations of each model were discussed and these models were also mutually compared based on their individual per- formance. The evaluation was executed on a real world dataset, provided by Home Credit company. Final performance of machine learning methods, measured by the KS and GINI metrics, was either very comparable or even worse than the performance of a traditional logistic regression. Still, the problem may lie in an insu cient dataset, in the improper data prepara- tion, or in inappropriately used algorithms, not necessarily in the models themselves.

Since the implementation of the Basel II framework in 2007, banks have been given the  opportunity to apply for the option to develop intern models for calculating their required capital. The purpose with this opportunity is that the capital requirements will correspond to the real risk exposure. This has been criticized, since there are incentives for the bank to do  an incorrect risk assessment intentionally and through that get a lower capital requirement. In this report we study how this opportunity affects the banks’ capitalization and if stricter capital requirements in fact leads to that the Swedish banks are better prepared for a financial crisis. The report also describes the risks that this opportunity to internal rating  causes. The study has been done by qualitative method where seven people, with different interests in the market, have been interviewed. By the answers given by the respondents and by earlier publications this report reveals that stronger capitalization is positive, but that  the Basel framework causes a risk that the banks intentionally underestimates  their risks. Nor is it possible to conclude that the banks are better prepared for a crisis afterthe implementation. This is because the IRB approach is something new and therefore  not optimized and yet balanced.

The banking industry in India has undergone many phases in its history; evolving from a regulated, decentralised system in the early 1800’s, to a regulated, centralised system during British rule, to a nationalised system following India’s independence, and finally a combination of a nationalised and private system adopting global standards as it currently stands. This study has two main aims. Firstly, it will assess the relationship between the firm-specific determinants of capital structure, based on the prevailing literature, and the capital structure of public and private sector banks in India. Secondly, it will determine whether there is a difference in the firm-specific factors that contribute to the determination of the capital structure of public sector banks and private sector banks. This study adopts quantitative methods, similar to previous studies on the relationship between capital structure and its firm-specific determinants. The dependent variable, being total leverage, is regressed against multiple independent variables, being profitability, growth, firm size and credit risk (hereinafter referred to as “risk” unless otherwise indicated) in a multivariate linear regression model. This study adds to the current literature by applying the same firm-specific independent variables to the case of private and public sector banks and then to evaluate and compare the similarities and differences between the regression outputs. The results show that for private sector banks, all independent variables are statistically significant in explaining total leverage, where all the independent variables conform to the current literature on capital structure – profitability (-), firm size (-), growth (+) and credit risk (-). Conversely, for public sector banks, all independent variables were considered to be statistically significant, except for credit risk – profitability (-), firm size (+) and growth (+). These results imply that credit risk is not an important determination in a nationalised banks’ capital structure; thus, providing evidence for the moral hazard theory of public sector banks.

iv Abstract This diploma thesis examines the impact of the COVID-19 crisis on the bank credit risk in the European Union. The analysis is performed using two sets of panel data. The first set contains data at the bank-level between 2012 and 2018 and is obtained from BankFocus batabase and the second set of data is obtained from the EBA Risk dashboard and contains data at the country-level between 2014 and 2020. Both datasets contain bank-specific variables and macroeconomic variables. We use the variables Cost of risk, Total capital ratio, Tier 1 ratio and NPE ratio as dependent variables. As representatives of the COVID-19 shock, we use the number of people infected with this disease, the number of deaths from this disease and the Stringency Index. We employ the GMM system for our analysis and test 5 hypotheses. We did not reject 3 hypotheses, namely that Cost of risk is a key determinant of credit risk and that the crisis caused by COVID-19 affects the variables Capitalo ratio and NPE ratio. We further concluded that the variables representing COVID-19 do not have a negative effect on credit risk, mainly due to the interventions of the ECB and the IASB. JEL Classification C12, C33, G01, G21 Keywords bank, COVID-19 crisis, credit risk management, Stringency index Title Author's e-mail Supervisor's e-mail...

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.

Under the Basel accords for measuring regulatory capital requirements, the set of credit risk parameters probability of default (PD), exposure at default (EAD) and loss given default (LGD) are measured with own estimates by the internal rating based approach. The estimated parameters are also the foundation of understanding the actual risk in a banks credit portfolio. The predictive performance of such models are therefore interesting to examine. The credit risk parameter LGD has been seen to give low performance for predictive models and LGD values are generally hard to estimate. The main purpose of this thesis is to analyse the predictive performance of a multi-gene genetic programming approach to symbolic regression compared to three benchmark regression models. The goal of multi-gene symbolic regression is to estimate the underlying relationship in the data through a linear combination of a set of generated mathematical expressions. The benchmark models are Logit Transformed Regression, Beta Regression and Regression Tree. All benchmark models are frequently used in the area. The data used to compare the models is a set of randomly selected, de-identified loans from the portfolios of underlying U.S. residential mortgage-backed securities retrieved from International Finance Research. The conclusion from implementing and comparing the models is that, the credit risk parameter LGD is continued difficult to estimated, the symbolic regression approach did not yield a better predictive ability than the benchmark models and it did not seem to find the underlying relationship in the data. The benchmark models are more user-friendly with easier implementation and they all requires less calculation complexity than symbolic regression.

Current regulation governing acquisition financing Abstract The thesis deals with financing of share deal acquisition operations. The first chapter points out idiosyncrasies of acquisition operations, providing context for the rest of the thesis. The second chapter deals with loan financing, one of the two financing methods described. The chapter starts off with Czech Civil Code's rules governing loans and goes on to provide an overview of stipulations used in corporate banking practice, including the usual arrangement of rights and obligations of lenders and borrower in case of syndicated loans. A subchapter about credit risk rounds off the second chapter. Not restricted to public law regulation, it describes derivatives used to hedge against credit risk and touches upon the influence of regulation on cost of loan financing. The third chapter is about bond financing. It offers a robust overview of Czech rules governing bonds as nominate debt securities and their issue. The consequent subchapter on placement of bonds handled by an investment firm serves as an equivalent of the banking practice- focused subchapter. Subjects that perform various tasks after placement in order to simplify the administration of a given issue are a topic which completes the third chapter. The conclusion of the thesis sets out...

The vast majority of the Swedish real estate companies are to some extent financed by debt and are dependent on external capital when expanding their business. Swedish real estate companies have traditionally financed their business through bank loans, but as a result of – among other things – stricter regulations, an increasing share of the Swedish real estate companies seek funding in the capital market, and corporate bonds in particular have emerged as an alternative to bank loans. In all types of lending, whether it is a bank loan or an investor who buys a bond, the lender must assess the credit risk of the company and / or the bond. This is to ensure the company's repayment ability and that the borrower gets sufficient compensation for the risk undertaken. In this thesis, the credit risk assessment process has been evaluated from two different perspectives to explore if there are any differences in the assessment conducted by banks and bond investors. In this thesis, it appears that the differences between the different parties' assessment are relatively small and that both parties evaluate approximately the same parameters and key performance indicators. / De allra flesta fastighetsbolag i Sverige finansierar sig delvis genom externt kapital och är beroende av nya krediter när de vill utöka sin verksamhet. Svenska fastighetsbolag har traditionellt sett finansierat sig via banklån men på grund av bland annat striktare regleringar väljer alltfler fastighetsbolag att söka finansiering på kapitalmarknaden, där framförallt företagsobligationer har växt fram som ett alternativ till bankfinansiering.  I alla typer av kreditgivning, oavsett om det handlar om banklån eller en investerare som köper en obligation, måste kreditgivaren göra en kreditriskbedömning av bolaget och/eller obligationen. Detta för att säkerställa bolagets återbetalningsförmåga och att långivaren får tillräcklig kompensation för den risk denne tar. I det här examensarbetet har kreditriskbedömningsprocessen utvärderats från två olika perspektiv för att se om det går att hitta några skillnader i bedömningen utförd av banker respektive obligationsinvesterare. Resultatet tyder på att skillnaderna mellan de olika parternas bedömning inte är särskilt stora utan båda parter utvärderar ungefär samma parametrar och nyckeltal.