Credit rating analysis with support vector machines and neural networks: A market comparative study

Zan Huang, Hsinchun Chen, Chia Jung Hsu, Wun Hwa Chen, Soushan Wu

Research output: Contribution to journalArticle

578 Scopus citations

Abstract

Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.

Original languageEnglish (US)
Pages (from-to)543-558
Number of pages16
JournalDecision Support Systems
Volume37
Issue number4
DOIs
StatePublished - Sep 1 2004

Keywords

  • Backpropagation neural networks
  • Bond rating prediction
  • Credit rating analysis
  • Cross-market analysis
  • Data mining
  • Input variable contribution analysis
  • Support vector machines

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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