A study of Taiwan's issuer credit rating systems using support vector machines

Wun Hwa Chen, Jen Ying Shih*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)


By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62%) is also higher than previous research.

Original languageEnglish
Pages (from-to)427-435
Number of pages9
JournalExpert Systems with Applications
Issue number3
Publication statusPublished - 2006 Apr
Externally publishedYes


  • Credit ratings
  • Support vector machines
  • Taiwan's banking industry

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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