Predicting Audit Failure Using Metaheuristic Algorithms

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.

Zarrin Saeed; Mohammad Ahmad; Zeynali Mehdi

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

Journal Paper

Paper Information

Journal: Management Strategies and Engineering Sciences Year:2024 | Volume:6 | Issue:3 Page(s): 20-31

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Predicting Audit Failure Using Metaheuristic Algorithms

Author(s)

Zarrin Saeed | Mohammad Ahmad | Zeynali Mehdi | Issue Writer Certificate

Keywords

Audit failure

Prediction

Metaheuristic algorithms

Abstract

The aim of the present study is to predict Audit failure using Metaheuristic algorithms in companies listed on the Tehran Stock Exchange. To achieve this objective, 1, 848 firm-year observations (154 companies over 12 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2011 to 2022. In this study, four Metaheuristic algorithms (including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Bee Colony Optimization (BCO)) were utilized, as well as two methods for selecting the final research variables (the two-sample t-test and the forward stepwise selection method) to create the model. The results from the Metaheuristic algorithms indicate that the overall accuracy of the GA, PSO, ACO, and BCO algorithms is 95. 3%, 94. 5%, 90. 6%, and 92. 8%, respectively, demonstrating the superiority of the Genetic Algorithm (GA) compared to other Metaheuristic algorithms. Furthermore, the overall results from the variable selection methods indicate the efficiency of the stepwise method. Therefore, in companies listed on the Tehran Stock Exchange, the stepwise method and the Genetic Algorithm (GA) provide the most efficient model for predicting Audit failure.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans