The main goal of this research is to accurately analyze the profit management using the neural networks and decision tree and comparing them with the linear models. For this purpose eleven variables effecting the earnings management as independent variables and discretionary accruals as a dependent variable have been used. In this research 55 companies from 2006 through 2009 were analyzed in a seasonal way. Regression Panel Method of linear model and Generalized Feed Forward network and CART were used through neural network and decision tree were used. The results of the research indicated that the neural network method and decision tree in the prediction of earnings management compared to the more precise linear methods and have a lower level of error. Meanwhile, earnings management with prior discretionary accruals and performance threshold and the firm performance, size, earnings persistence in both methods has the highest connection.
Hejazi, R., Mohamadi, S., aslani, Z., & Aghajani, M. (2012). Earnings Management Prediction Using Neural Networks and Decision Tree in TSE. Accounting and Auditing Review, 19(2), 31-46. doi: 10.22059/acctgrev.2012.29198
MLA
Rezvan Hejazi; Shapoor Mohamadi; zahra aslani; Majid Aghajani. "Earnings Management Prediction Using Neural Networks and Decision Tree in TSE", Accounting and Auditing Review, 19, 2, 2012, 31-46. doi: 10.22059/acctgrev.2012.29198
HARVARD
Hejazi, R., Mohamadi, S., aslani, Z., Aghajani, M. (2012). 'Earnings Management Prediction Using Neural Networks and Decision Tree in TSE', Accounting and Auditing Review, 19(2), pp. 31-46. doi: 10.22059/acctgrev.2012.29198
VANCOUVER
Hejazi, R., Mohamadi, S., aslani, Z., Aghajani, M. Earnings Management Prediction Using Neural Networks and Decision Tree in TSE. Accounting and Auditing Review, 2012; 19(2): 31-46. doi: 10.22059/acctgrev.2012.29198
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