The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GHADERI EGHBAL; AMINI PEYMAN; Mohammadi Mlqrny Ataullah; Norvash Iraj

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: FINANCIAL ACCOUNTING Year:2018 | Volume:10 | Issue:39 Page(s): 82-110

Download Full-Text

Persian Verion

View:

1,135

Download:

Cites:

Information Journal Paper

Title

The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management

Author(s)

GHADERI EGHBAL | AMINI PEYMAN | Mohammadi Mlqrny Ataullah | Norvash Iraj | Issue Writer Certificate

Keywords

Earnings ManagementQ2

Artificaial Neural NetworkQ2

Ant Colony OptimizationQ1

Abstract

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.

Cites

No record.

References

No record.

Cite

APA: Copy

GHADERI, EGHBAL, AMINI, PEYMAN, Mohammadi Mlqrny, Ataullah, & Norvash, Iraj. (2018). The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management. FINANCIAL ACCOUNTING, 10(39 ), 82-110. SID. https://sid.ir/paper/168262/en

Vancouver: Copy

GHADERI EGHBAL, AMINI PEYMAN, Mohammadi Mlqrny Ataullah, Norvash Iraj. The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management. FINANCIAL ACCOUNTING[Internet]. 2018;10(39 ):82-110. Available from: https://sid.ir/paper/168262/en

IEEE: Copy

EGHBAL GHADERI, PEYMAN AMINI, Ataullah Mohammadi Mlqrny, and Iraj Norvash, “The Accuracy of Artificial Neural Network and Ant Colony Optimization algorithm in predicting profit management,” FINANCIAL ACCOUNTING, vol. 10, no. 39 , pp. 82–110, 2018, [Online]. Available: https://sid.ir/paper/168262/en

Related Journal Papers