Credit Rating Analysis With Support Vector Machines and Neural Networks: A Market Comparative Study (2004)
| Citations: | 27 - 0 self |
BibTeX
@MISC{Huang04creditrating,
author = {Zan Huang and Hsinchun Chen and Chia-jung Hsu and Wun-hwa Chen and Soushan Wu},
title = {Credit Rating Analysis With Support Vector Machines and Neural Networks: A Market Comparative Study},
year = {2004}
}
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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.
