Measurement of Credit Rating Levels in the Face of Internal Control Requirements with a Comparative Approach Using Particle Swarm Optimization and Genetic Algorithms

Abstract

Objective:  The objective of this study is to evaluate the impact of internal control requirements on the credit ratings of companies listed on the Tehran Stock Exchange.

Methodology: The research utilizes a comparative approach, employing Particle Swarm Optimization (PSO) and Genetic Algorithms to analyze the credit ratings of 101 companies listed on the Tehran Stock Exchange over a 14-year period from 2006 to 2019. The companies were categorized based on whether their independent auditors expressed opinions on internal controls. Descriptive and statistical analyses were conducted to examine the changes in credit ratings before and after the implementation of the internal control requirements.

Findings: The results of the analysis indicate no significant difference in the accuracy of the credit rating predictions made by the two algorithms. Additionally, no notable difference was observed in the credit ratings of the companies before and after the implementation of internal control requirements. This suggests that the internal control regulations did not significantly influence the credit ratings of the companies in the sample.

Conclusion: The study concludes that internal control requirements do not appear to have a substantial impact on the credit ratings of companies listed on the Tehran Stock Exchange. The findings suggest that credit rating agencies may not have experienced significant information asymmetry before the implementation of these requirements, leading to stable credit ratings even after the regulations were enforced. The study recommends a review of the current internal control standards to enhance their effectiveness in improving financial reporting quality and ensuring legal accountability.