基于数据挖掘的重大错报风险识别和评估研究
    点此下载全文
引用本文:徐 静,李俊林.基于数据挖掘的重大错报风险识别和评估研究[J].财经理论与实践,2022,(6):79-85
摘要点击次数: 56
全文下载次数: 0
作者单位
徐 静,李俊林 (北京联合大学 管理学院北京 100101) 
中文摘要:基于现代风险导向审计理论,运用数据挖掘方法,选取2001-2020年因财务报表重大错报被出具保留和否定意见的上市公司为样本,构建基于CHAID算法的重大错报分类预测模型,从中识别重大错报的基本特征,挖掘重大错报与否的决策规则。结果表明:流动性差、盈利能力不足、长期偿债能力弱的上市公司,在较高的置信水平上存在高重大错报风险;模型总体正确率为97.17%,分类效果较为理想。鉴于此,能够为识别、评估和预测财务报表重大错报风险提供线索。
中文关键词:数据挖掘  重大错报风险  CHAID算法  分类预测  决策规则
 
Identification and Assessment of Risk of Material Misstatement Based on Data Mining
Abstract:Based on the modern risk-oriented auditing theory and data mining method, taking the listed companies with reservations and negative opinions due to material misstatement from 2001 to 2020 as the sample, a classification and prediction model of material misstatement based on CHAID algorithm is built. From which it’s able to identify the basic characteristics of material misstatement, and mine the decision-making rules of whether or not to make material misstatement. The results show that listed companies with poor liquidity, insufficient profitability and weak long-term solvency show high risk of material misstatement (RMM) at a high confidence level. And as well, the overall accuracy of the model is 97.17%, which indicates that the classification effect is ideal. In this regard, clues for identifying, assessing and predicting the RMM of financial statements are provided.
keywords:data mining  risk of material misstatement  CHAID algorithm  classification prediction  decision rule
查看全文   查看/发表评论   下载pdf阅读器