基于变精度加权平均粗糙度决策树的财务预警研究

运用聚类方法把公司财务状况分为5个等级,分别为财务状况健康,良好,一般,预警和危机,与以往将研究样本分为ST和非ST两类的财务预警模型相比,5分类模型更加精确合理,贴近实际。同时基于指标相关性和指标重要度对33个财务指标进行了约简,得到9个能够反映企业财务状况的财务指标。以约简后的9个指标及5个等级的财务状况来建立决策树,指标体系和财务等级更加合理。树的生成过程运用粗糙集中的变精度加权平均粗糙度作为选择测试属性的方法,每次选择变精度加权平均粗糙度值最小的属性作为分支结点。变精度加权平均粗糙度的应用提高了决策树的防噪声能力,复杂性较低且能有效提高分类效果。实证研究表明将它应用到财务预警领域,提高了财务预警的分类精度。

Financial Early-warning Based on Variable Precision Weighted Average Roughness Decision Tree

Traditional researches usually divide the samples into ST and non-ST for the financial status. We use clustering methods to divide sample companies into 5 categories, namely, healthy, good, general, warning and crisis. This five-level classification may be more reasonable and practical than the traditional two-level classification. Meanwhile, 33 financial indicators are reduced into 9 ones based on the indicator correlations and the significance. The reduced indicaor system and the five-level financial status are used to construct the decision tree, which makes the process more reasonable. Then, we regard variable precision-weighted average roughness as the method of selecting branch properties, and each time select minimum of them as branch properties. Thus generated decision tree can avoid the detailed classification of a small amount of special categories data and improve the ability of anti-noise. This method is less complex and can effectively improve the classification results. The empirical study proves to have good results by using it in the early-warning of financial distress.