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A Simplified Variable Analysis of Credit Ratings for Small Chinese Enterprises Based on Support Vector Machine

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  • Ying Chen
  • Yangkai Guo
  • Maoguo Wu

Abstract

Small enterprises are an important component of the national economy and valuable customers of commercial banks. Commercial banks use credit ratings, including financial and nonfinancial indices, to analyze small enterprises before committing to long-term collaborations, including loans. This paper uses a support vector machine algorithm to establish an imbalanced multi-classification model and compares the results to those of other methods. Commercial banks need simplified variable analysis credit ratings that use minimal information to rapidly and accurately obtain credit ratings and improve the efficiency of the process. Accordingly, we perform multiple tests of simplified rating systems using fewer variables.

Suggested Citation

  • Ying Chen & Yangkai Guo & Maoguo Wu, 2020. "A Simplified Variable Analysis of Credit Ratings for Small Chinese Enterprises Based on Support Vector Machine," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(6), pages 1-45, June.
    • Handle: RePEc:ibn:ijefaa:v:12:y:2020:i:6:p:45
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    References listed on IDEAS

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    3. Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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