基于数据挖掘的重大错报风险识别和评估研究

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

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

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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

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