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

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  • Evzen Kocenda

    ()

  • Martin Vojtek

    ()

Abstract

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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Bibliographic Info

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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Keywords: credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;

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References

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  1. Evžen Koèenda & Jan Hanousek & Peter Ondko, 2007. "The Banking Sector in New EU Member Countries: A Sectoral Financial Flows Analysis (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, Charles University Prague, Faculty of Social Sciences, vol. 57(5-6), pages 200-224, August.
  2. Ranjula Bali Swain, 2007. "The demand and supply of credit for households," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 39(21), pages 2681-2692.
  3. Alexis Derviz & Jiří Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., M.E. Sharpe, Inc., vol. 44(1), pages 117-130, January.
  4. Gabrisch, Hurbert & Orlowski, Lucjan, 2009. "Interest Rate Convergence in the Euro-Candidate Countries: Volatility Dynamics of Sovereign Bond Yields," Working Papers 2009001, Sacred Heart University, John F. Welch College of Business.
  5. José Luis Gallizo & Ramon Saladrigues & Manuel Salvador, 2010. "Financial Convergence in Transition Economies: EU Enlargement," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., M.E. Sharpe, Inc., vol. 46(3), pages 95-114, May.
  6. Ceyla Pazarbasioglu & Gudrun Johnsen & Paul Louis Ceriel Hilbers & Inci Ötker, 2005. "Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies," IMF Working Papers 05/151, International Monetary Fund.
  7. David A Grigorian & Vlad Manole, 2006. "Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis," Comparative Economic Studies, Palgrave Macmillan, vol. 48(3), pages 497-522, September.
  8. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, Elsevier, vol. 30(3), pages 851-873, March.
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Cited by:
  1. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers, Czech National Bank, Research Department 2012/12, Czech National Bank, Research Department.
  2. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris West - Nanterre la Défense, EconomiX.
  3. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.

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