EXPLAINING THE MODEL OF EARNING MANAGEMENT MEASUREMENT USING AN INTELLIGENT HYBRID METHOD OF NEURAL NETWORKS AND META HEURISTIC ALGORITHMS (GENETIC AND PARTICLE SWARM OPTIMIZATION)

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GHADERI EGHBAL; AMINI PEYMAN; NORAVESH IRAJ; MOHAMMADI ATA

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Journal: FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT) Year:2018 | Volume:9 | Issue:36 Page(s): 99-127

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Title

EXPLAINING THE MODEL OF EARNING MANAGEMENT MEASUREMENT USING AN INTELLIGENT HYBRID METHOD OF NEURAL NETWORKS AND META HEURISTIC ALGORITHMS (GENETIC AND PARTICLE SWARM OPTIMIZATION)

Author(s)

GHADERI EGHBAL | AMINI PEYMAN | NORAVESH IRAJ | MOHAMMADI ATA | Issue Writer Certificate

Keywords

EARNINGS MANAGEMENTQ2

NEURAL NETWORKQ2

GENETIC ALGORITHMQ2

PARTICLE SWARM OPTIMIZATIONQ1

Abstract

Undrestanding the earning management for the users of accounting information due to performance evaluation, profitability forecast and detrmining the value of the company is very important.The purpose of this research is to estimate the a model for earning management using NEURAL NETWORK model and then the use of GENETIC ALGORITHM, and PARTICLE SWARM OPTIMIZATION to find a better combination of input data, so that it can optimize the initial model. For this purpose, 28 effective variables were used in the from of four groups (Financial, managerial, corporative and audit) during the years 2010 to 2016 in the companies admitted to the Tehran stock Exchange. The results showed that application of this algorithm has increased the efficiency of the model.Also, the evaluation of the performance of NEURAL NETWORK patterns suggests the absolute superiority of this pattern compared to the time linear method (LR).Combined method (A-PSO) and (A-GA)by identifying four optimal variables respectively precision forecast, shareholding of major shareholders, company size and the ratio of the quality of earning management are carefully predicted respectively (%95.59) and (%94.75). In addition to the above mentioned intelligent methods, by improving correlation coefficient and error squares mean criterion compared to linear methods (LR) and NEURAL NETWORK method (ANN) in predicting group results, management and corporate features are more efficient.

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APA: Copy

GHADERI, EGHBAL, AMINI, PEYMAN, NORAVESH, IRAJ, & MOHAMMADI, ATA. (2018). EXPLAINING THE MODEL OF EARNING MANAGEMENT MEASUREMENT USING AN INTELLIGENT HYBRID METHOD OF NEURAL NETWORKS AND META HEURISTIC ALGORITHMS (GENETIC AND PARTICLE SWARM OPTIMIZATION). FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 9(36 ), 99-127. SID. https://sid.ir/paper/197531/en

Vancouver: Copy

GHADERI EGHBAL, AMINI PEYMAN, NORAVESH IRAJ, MOHAMMADI ATA. EXPLAINING THE MODEL OF EARNING MANAGEMENT MEASUREMENT USING AN INTELLIGENT HYBRID METHOD OF NEURAL NETWORKS AND META HEURISTIC ALGORITHMS (GENETIC AND PARTICLE SWARM OPTIMIZATION). FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)[Internet]. 2018;9(36 ):99-127. Available from: https://sid.ir/paper/197531/en

IEEE: Copy

EGHBAL GHADERI, PEYMAN AMINI, IRAJ NORAVESH, and ATA MOHAMMADI, “EXPLAINING THE MODEL OF EARNING MANAGEMENT MEASUREMENT USING AN INTELLIGENT HYBRID METHOD OF NEURAL NETWORKS AND META HEURISTIC ALGORITHMS (GENETIC AND PARTICLE SWARM OPTIMIZATION),” FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), vol. 9, no. 36 , pp. 99–127, 2018, [Online]. Available: https://sid.ir/paper/197531/en

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