Work detail

Performance Ranking of Czech Credit Scoring Models

Author: Mgr. Peter Smolár
Year: 2020 - summer
Leaders: prof. PhDr. Tomáš Havránek Ph.D.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 50
Awards and prizes:
Abstract: This thesis provides a comprehensive ranking of 11 Czech statistical and 4 foreign credit
scoring models. The ranking is based on the predictive performance of individual models, as
measured by the area under curve, evaluated on a randomly sampled set of 250 training and
validation samples. After establishing a baseline comparison, 3 avenues of estimation setup
optimization are explored, namely missing value treatment, estimation method and the use of
additional non-financial variables. After being optimized, the models are once again ranked
based on their predictive performance. Statistical inference is drawn using ANOVA and the
Friedman test, along with the corresponding Tukey and Nemeyi pos-hoc tests. In their baseline
form, the Czech credit scoring models are found to be outperformed by the foreign benchmark
model. Treating the missing values by OLS imputation and estimating the models by probit,
significantly is found to significantly improve their predictive performance. In their optimized
form, the difference in predictive performance between Czech and foreign credit scoring model
is found to be only margina




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