Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11681960_27 | MDAI |
Keywords | Field | DocType |
sample data,support vector machine,fisher discriminant analysis,generalization ability,three-year data,model stability,fitting ability,financial distress prediction,financial distress,svm model,better balance,early warning,logistic regression,cross validation,empirical study | Regression analysis,Computer science,Support vector machine,Artificial intelligence,Linear discriminant analysis,Backpropagation,Artificial neural network,Cross-validation,Logistic regression,Machine learning,Empirical research | Conference |
Volume | ISSN | ISBN |
3885 | 0302-9743 | 3-540-32780-0 |
Citations | PageRank | References |
31 | 1.06 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Feng Hui | 1 | 56 | 3.27 |
| Jie Sun | 2 | 60 | 3.34 |