集成学习算法——上市公司财务舞弊与违规管理研究分析

Q1 Decision Sciences
Weihong Li, Xiujuan Xu
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引用次数: 0

摘要

近年来,尽管对上市公司财务造假和违规行为进行了严格的“零容忍”打击,但财务造假、夸大营收和利润、涉嫌欺诈的案件仍不断曝光。本研究首先建立了财务舞弊指标体系,并利用XGBoost算法构建了上市公司财务舞弊及违规行为的预测模型。选择指标并输入到模型中。获得了实验数据集。将XGBoost算法与另外两种算法进行了比较。接收算子特征曲线(receiver operator characteristic, ROC)表明,XGBoost算法在三种算法中预测性能最好。结果表明,XGBoost算法的准确率为93.17%,召回率为92.23%,其值为0.9270,曲线下面积为0.90,优于基于梯度提升决策树(Gradient boosting Decision Tree, GBDT)算法和logistic算法的预测模型。综合各评价指标的数据发现,采用XGBoost算法构建的财务欺诈与违规预测模型的预测效果最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble learning algorithm - research analysis on the management of financial fraud and violation in listed companies
In recent years, despite the strict "zero tolerance" crackdown on the financial fraud and violation behavior of listed companies, the cases of financial fraud, revenue and profit overstatement, and suspected fraud have continued to be exposed. This study first established a financial fraud index system and used the XGBoost algorithm to construct a prediction model for financial fraud and violations of listed companies. The indicators were selected and input into the model. A dataset was obtained for experiments. The XGBoost algorithm was compared with two other algorithms. The receiver operator characteristic (ROC) curves showed that the XGBoost algorithm had the best prediction performance among the three algorithms. It was found that the precision of the XGBoost algorithm was 93.17%, the recall rate was 92.23%, the value was 0.9270, and the area under the curve was 0.90, indicating a better performance than the prediction models based on the Gradient Boosted Decision Tree (GBDT) algorithm and the Logistics algorithm. Considering the data of various evaluation indicators, it is found that the predictive effect of the financial fraud and violation prediction model built by the XGBoost algorithm is the best.
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来源期刊
Decision Making Applications in Management and Engineering
Decision Making Applications in Management and Engineering Decision Sciences-General Decision Sciences
CiteScore
14.40
自引率
0.00%
发文量
35
审稿时长
14 weeks
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