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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Ensemble learning methods for scoring models development
 Nožička, Michal ; Witzany, Jiří (advisor) ; Cipra, Tomáš (referee)
Credit scoring is very important process in banking industry during which each potential or current client is assigned credit score that in certain way expresses client's probability of default, i.e. failing to meet his or her obligations on time or in full amount. This is a cornerstone of credit risk management in banking industry. Traditionally, statistical models (such as logistic regression model) are used for credit scoring in practice. Despite many advantages of such approach, recent research shows many alternatives that are in some ways superior to those traditional models. This master thesis is focused on introducing ensemble learning models (in particular constructed by using bagging, boosting and stacking algorithms) with various base models (in particular logistic regression, random forest, support vector machines and artificial neural network) as possible alternatives and challengers to traditional statistical models used for credit scoring and compares their advantages and disadvantages. Accuracy and predictive power of those scoring models is examined using standard measures of accuracy and predictive power in credit scoring field (in particular GINI coefficient and LIFT coefficient) on a real world dataset and obtained results are presented. The main result of this comparative study is that...
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Basic approaches to robust conditional value at risk
 Nožička, Michal ; Branda, Martin (advisor) ; Petrová, Barbora (referee)
The work describes conditional value at risk, its robustification with respect to the probability distribution of yields of assets and its applications to optimal portfolio selection. In chapter one there are definitions of conditional value at risk and its generalization throught robustification and also motivation to these definitions. The basic properties of conditional value at risk, mainly coherence and continuity with respect to the parametr of confidence level, are discussed in chapter two. There is also shown that some of these properties are preserved after robustification. The third chapter is dedicated to the derivation of optimization problems of optimal portfolio selection on the basis of conditional value at risk and its robustification. This thesis describes only special cases so that the final problems are solveble by the means of linear programming. The fourth chapter describes particular utilization of these methods with usage of real data from financial markets. Powered by TCPDF (www.tcpdf.org)
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