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Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

  • Evzen Kocenda

    ()

  • Martin Vojtek

    ()

Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.

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File URL: http://www.wdi.umich.edu/files/Publications/WorkingPapers/wp1015.pdf
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Paper provided by William Davidson Institute at the University of Michigan in its series William Davidson Institute Working Papers Series with number wp1015.

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Length: pages
Date of creation: 01 Apr 2011
Date of revision:
Handle: RePEc:wdi:papers:2011-1015
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