An Application of Support Vector Machine to Companies’ Financial Distress Prediction
Lecture notes in computer sciencePublished 1 January 2006
Xiaofeng Hui, Jie Sun
Citations64
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
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.
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.
Keywords
Computer ScienceBusiness, Management and AccountingEngineering
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