Predicting Audit Failure Using 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.