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| 基于概率神经网络的欺诈性财务报告的识别研究 | |
| 引用本文： | 蒙肖莲,李金林,杨毓.基于概率神经网络的欺诈性财务报告的识别研究[J].数理统计与管理,2009,28(1). |
| 作者姓名： | 蒙肖莲 李金林 杨毓 |
| 作者单位： | 1. 北京理工大学管理与经济学院,北京,100081;南京理工大学经济管理学院,江苏,南京,210094 2. 北京理工大学管理与经济学院,北京,100081 3. 中国建设银行河北分行,河北,石家庄,050000 |
| 基金项目： | 中国博士后科学基金，教育部社会科学基金 |
| 摘 要： | 本文探索概率神经网络PNNs(Probabilistic Neural Networks)在构建欺诈性财务报告识别模型方面的有效性,重点探讨了PNN模型变量的选择及平滑参数的确定问题,同时将所提出模型的性能和人工神经网络(ANNs)、logit回归模型的性能进行了比较.结果证明,PNN模型具有很高的预测力,并发现该模型的性能优于ANN模型以及logit回归模型. |
| 关 键 词： | 概率神经网络 欺诈性财务报告 识别模型 |
Fraudulent Financial Statements Identification Using Probabilistic Neural Networks |
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| MENG Xiao-lian,LI Jin-lin,YANG Yu.Fraudulent Financial Statements Identification Using Probabilistic Neural Networks[J].Application of Statistics and Management,2009,28(1). | |
| Authors: | MENG Xiao-lian LI Jin-lin YANG Yu |
| Abstract: | This paper uses Probabilistic neural networks(PNNs)for the development of a model that detects firms issuing fraudulent financial statements(FFS).Focusing on how to select the variables that can identify FFS best and specification of the smoothing parameter of the model,a comparative analysis on efficiency of PNNs with artificial neural networks(ANNs)and logistic regression were also made.The results demonstrate the high explanatory power of the PNN model in identifying fraudulent financial statements.The model is also found to outperform traditional ANN model,as well as logistic regression. |
| Keywords: | probabilistic neural networks fraudulent financial statements identifying model |
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