دانلود ترجمه مقاله بررسی کاربرد شبکه عصبی در مدیریت سود

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  A large body of studies has examined the occurrence of earnings management in various contexts. Inmost 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 approachfor 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. Thepurpose of this study is to assess whether neural network-based models outperform linear and piecewiselinear-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 regres sion-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.