Development of the Beneish Model by Combining Artificial Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction

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Asgari Alouj Hosein; NIKBAKHT MOHAMMADREZA; KARAMI GHOLAMREZA; Momeni Mansor

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Journal: THE IRANIAN ACCOUNTING AND AUDITING REVIEW Year:2020 | Volume:26 | Issue:4 Page(s): 615-638

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Title

Development of the Beneish Model by Combining Artificial Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction

Author(s)

Asgari Alouj Hosein | NIKBAKHT MOHAMMADREZA | KARAMI GHOLAMREZA | Momeni Mansor | Issue Writer Certificate

Keywords

Particle swarm optimization algorithmQ1

Product competition marketQ1

Artificial neural networkQ2

Benish modelQ1

Information environmentQ1

Abstract

very low confidence range of 0/5-0/6, indicating failed test result and high prediction error up to 39/74 percent. Consequently the best cut-off point and the best precision for BM were estimated to be 0/5021, 60/26 percent, by the maximum accuracy method, respectively. Furthermore, the results show that the AUC for DBM was increased to 0/6335 through incorporating environmental variables of Product Market Competition (PMC) and information symmetry (IS) to the BM, which is still out of an acceptable range of 0/7– 0/8 for a relatively good test, indicating poor test result and high model prediction error up to 32/58 percent. Consequently the best cut-off point and the best precision for DBM were estimated to be 0/5304, 67/42 percent by the intersection point of minimum distance and Youden's index, respectively. Incorporating PMC and IS variables to the original model of Beneish decreased model prediction error from 39/74 to 32/58 percent, which is not statistically significant. Nevertheless, this fact improved the predictive power of the BM slightly insignificant. Conclusion: The findings indicate that the BM is a random model in Iranian capital market and impotent to detect two groups of earning manipulator and nonearning manipulator companies. Although findings indicate that the DBM is a little bit more powerful than the BM and confirm that the impact of environmental variables of PMC and IS is slightly insignificant, indicating the test outcome is still weak and the DBM is an approximately random model in identifying two groups of earning manipulator and non-earning manipulator companies.

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APA: Copy

Asgari Alouj, Hosein, NIKBAKHT, MOHAMMADREZA, KARAMI, GHOLAMREZA, & Momeni, Mansor. (2020). Development of the Beneish Model by Combining Artificial Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction. THE IRANIAN ACCOUNTING AND AUDITING REVIEW, 26(4 ), 615-638. SID. https://sid.ir/paper/8132/en

Vancouver: Copy

Asgari Alouj Hosein, NIKBAKHT MOHAMMADREZA, KARAMI GHOLAMREZA, Momeni Mansor. Development of the Beneish Model by Combining Artificial Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction. THE IRANIAN ACCOUNTING AND AUDITING REVIEW[Internet]. 2020;26(4 ):615-638. Available from: https://sid.ir/paper/8132/en

IEEE: Copy

Hosein Asgari Alouj, MOHAMMADREZA NIKBAKHT, GHOLAMREZA KARAMI, and Mansor Momeni, “Development of the Beneish Model by Combining Artificial Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction,” THE IRANIAN ACCOUNTING AND AUDITING REVIEW, vol. 26, no. 4 , pp. 615–638, 2020, [Online]. Available: https://sid.ir/paper/8132/en

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