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

Wun Hwa Chen, Jen Ying Shih

    Research output: Contribution to journalArticlepeer-review

    68 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


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

    ASJC Scopus subject areas

    • Engineering(all)
    • Computer Science Applications
    • Artificial Intelligence


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