Detecting earnings management with neural networks

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• 作者：Henrik H&#246 ; glund ; ^{henrik.hoglund@hanken.fi}
• 关键词：Earnings management ; Discretionary accruals ; Neural networks
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：August, 2012
• 卷：39
• 期：10
• 页码：9564-9570
• 文件大小：373 K

摘要

A large body of studies has examined the occurrence of earnings management in various contexts. In most studies, the assumption has been that earnings are managed through accounting accruals. Thus, a range of accrual based earnings management detection models have been suggested. The ability of these models to detect earnings management has, however, been questioned in a number of studies. An explanation to the poor performance of the existing models is that most models use a linear approach for modeling the accrual process even though the accrual process has in fact proven non-linear in several studies. An alternative way to deal with the non-linearity is to use various types of neural networks. The purpose of this study is to assess whether neural network-based models outperform linear and piecewise linear-based models in detecting earnings management. The study comprises neural network models based on a self-organizing map (SOM), a multilayer perceptron (MLP) and a general regression neural network (GRNN). The results show that the GRNN-based model performs best, whereas the linear regression-based model has the poorest performance. However, the results also show that all five models assessed in this study estimate discretionary accruals, a proxy for earnings management, with some bias.