An Empirical Study of Financial Distress Prediction of Listed Companies Based on Support Vector Machine

摘要：

Based on the drawbacks of the traditional prediction model, this paper discusses the role of Support Vector Machines model in the financial distress prediction of Listed Companies. By some empirical comparison and model analysis of Support Vector Machines(SVM for short), Multiple Discriminant Analysis(MDA for short), Multiple Logistic Analysis(MLA for short) and BP Artificial Neural Networt (BP-ANN for short), we conclude that the average prediction error rate calculated from the 20 groups of testing samples is the lowest by SVM, and is notably advantageous to MDA, MLA and BP-ANN, we also prove the effectiveness and superiority of SVM model in financial distress prediction.

关键词： SVM MDA MLA BP-ANN Financial Distress Listed Company

作者: ZHAO Guan hu LIN Qian

作者单位: School of Management, Tianjin University, Tianjin, P.R.China, 300072 School of Accounting, Shandong University of Finance, Jinan, P.R.China, 250014

会议类型: 国际会议

会议名称: 2008年国际应用统计学术研讨会(2008 International Institute of Applied Statistics Studies)

会议地点: 烟台

会议语种:英文

页码: 1-5

在线出版日期: 2008-08-14（万方平台首次上网日期，不代表论文的发表时间）