An application of support vector machine to companies' financial distress prediction

Authors:
Xiao-Feng Hui;Jie Sun
Affiliations:
School of Management, Harbin Institute of Technology, Harbin, HeiLongJiang Province, China;School of Management, Harbin Institute of Technology, Harbin, HeiLongJiang Province, China
Venue:
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Year:
2006

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Abstract

Because of the importance of companies' financial distress prediction, this paper applies support vector machine (SVM) to the early-warning of financial distress. Taking listed companies' three-year data before special treatment (ST) as sample data, adopting cross-validation and grid-search technique to find SVM model's good parameters, an empirical study is carried out. By comparing the experiment result of SVM with Fisher discriminant analysis, Logistic regression and back propagation neural networks (BP-NNs), it is concluded that financial distress early-warning model based on SVM obtains a better balance among fitting ability, generalization ability and model stability than the other models.