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Checking account activity and credit default risk of enterprises: An application of statistical learning methods

Author

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  • Jinglun Yao
  • Maxime Levy-Chapira
  • Mamikon Margaryan

Abstract

The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios using the logistic regression. As one of the major commercial banks in France, we have access to the the account activities of corporate clients. We show that this transactional data outperforms classical financial ratios in predicting the default event. As the new data reflects the real time status of cash flow, this result confirms our intuition that liquidity plays an important role in the phenomenon of default. Moreover, the two data sets are supplementary to each other to a certain extent: the merged data has a better prediction power than each individual data. We have adopted some advanced machine learning methods and analyzed their characteristics. The correct use of these methods helps us to acquire a deeper understanding of the role of central factors in the phenomenon of default, such as credit line violations and cash inflows.

Suggested Citation

  • Jinglun Yao & Maxime Levy-Chapira & Mamikon Margaryan, 2017. "Checking account activity and credit default risk of enterprises: An application of statistical learning methods," Papers 1707.00757, arXiv.org.
  • Handle: RePEc:arx:papers:1707.00757
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    File URL: http://arxiv.org/pdf/1707.00757
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    3. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
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    7. Lars Norden & Martin Weber, 2010. "Credit Line Usage, Checking Account Activity, and Default Risk of Bank Borrowers," Review of Financial Studies, Society for Financial Studies, vol. 23(10), pages 3665-3699, October.
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    9. repec:bla:joares:v:4:y:1966:i::p:71-111 is not listed on IDEAS
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