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The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management
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Eghbal Ghaderi1   , Pyman Amini *2 , Ataullah Mohammadi Mlqrny2 , Iraj Norvash |
1- Student
2- Academic staff |
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Abstract: (3057 Views) |
Undrestanding the quality of profits for users of accounting information is very important because of performance appraisal, profitability forecasting, and the determination of corporate value. The purpose of this study is to examine the accuracy of forecasting earnings management using Artificial Neural Networks (ANN) and cluster Ant Colony Optimization (ACO) algorithms and compare it with linear models (LR). For this purpose, 28 variabels that affect the management of earnings in four groups (Financial, Managerial, Corporate and Auditing) have been accepted in 124 companies during the years 2010 to 2016 and were used Tehran Stock Exchange. The overall results of this study shows that artificial neural network and ant colony optimization algorithm in predicting profit management is more accurate than linear method with less error rate. Also, the accuracy of artificial neural network composition and ant colony algorithm(A-ANN), suggests the superiority of this pattern compared to artificial neural network method. The results of the combination of artificial neural network- ant colony optimization algorithm with correlation coefficient (0/878) shows that this model has the ability to predict management with 97 percent accurancy with six predictive variables, accurancy of forecasting, sharehlding of maior shareholders, profitability, fluctuations in profit, company’s age and size.
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| Keywords: Earnings Management, Artificaial Neural Network, Ant Colony Optimization. |
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Full-Text [PDF 1143 kb]
(2242 Downloads)
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Type of Study: Research |
Subject:
Special
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