Earnings management prediction using Neural Network and decision tree Agriculture and textile industries listed companies on the Tehran Stock Exchange

Today, quantitative methods to predict the most important tools for decision making huge investments in the market and capitalism, have become.Theprediction method is the most importantBlanks.The main objectivethis study accurately predicted earnings management using neural networks and decision tree is compared with linear models.For this purpose, nine variables that affect earnings management and accruals as independent variables, was used as the dependent variable.In this study, both agriculture and the textile industry were reviewed. Methods for linear least squares regression and a feedforward neural network and decision tree data mining techniques were used by to evaluate Cart.The results of this study showed that the neural network approach and linear decision tree methods adopted towards more accurate prediction of earnings management with error level is less obvious. Relationships between the dependent variables with the independent variables can be said,Discretionary accruals prior period earnings management variables (DAI), nondiscretionary accruals Prioror threshold function (THOD) and pay for performance sensitivity (PPS) method, regression, neural networks, Sart is most relevant.