بررسی مقایسه‌ ای عملکرد مدل شبکه عصبی با مدل‌ های رگرسیونی خطی چندبخشی …

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Abstract
Most of earning management detection models are based on accounting accruals, as it is assumed that earnings are managed through accounting accruals. The ability of these models to detect earning management has, however, been questioned in a number of studies and most of them have showed a poor performance for these models. An explanation to the poor performance of the existing models is that most models use a linear approach for modeling the accrual process. Emeging new models with the ability of analysing nonlinear complicated relationship help us to test again the relevance between variables affecting earning management and improve the process of detecting it. In this study we use a sample made up of 1408 firm-year (including 176 firms from 1983 to 1990). The total data set is seprated into a test and a trian data set. This study comprises three models: Multilayer Perceptron (as a neural network model), linear regression and a piecewise linear regression. Our analysis is based on discretionary accrual, estimated by these models according to foyr operation variables. The result showes that the MLP model has better performance than other two models.
Keywords: Earning Management, Neural Network, Discretionary Accruals.
Faculty of Management and Econom