作　　者： ; ; ;

机构地区： 广东金融学院

出　　处： 《广西财经学院学报》 2014年第6期77-83,共7页

摘　　要： 上市公司财务预警模型受到不同配对比例的下采样影响较大,2007—2008年上市公司财务数据的分析结果表明:配对比例过高,ST公司的识别率太低;配对比例过低,模型识别结果变异太大,结果不可靠;而现代统计学中针对不平衡数据的统计方法 SMOTO方法和Bagging算法均能较好地克服样本比例不均衡的影响,上述数据的实证研究结果显示:基于上述两种方法的财务预警模型在测试集上对正常公司和ST公司都取得了较好的稳定识别率。 Financial distress early-warning models chosen by listed companies are affected significantly by different matching ratios. Through the analysis of the effects of the corporation financial data collected be-tween 2007 and 2008,the study finds that with higher matching ratios come lower identification rates among ST companies,while lower matching ratios seem to lead to greater variations in model identification and thus bring about unreliable results. In view of imbalanced data set,the SMOTO and Bagging algorithm methods are often applied in modern statistics aiming to minimize the effects of imbalanced sample proportion. The results of the above-mentioned empirical study show that the early-warning models based on the two meth-ods in the dataset test have achieved a steady recognition rate in normal and ST corporations respectively.

关 键 词： 财务预警 样本配比 算法

领　　域： [经济管理]