Credit Rating of Companies listed on the Tehran Stock Exchange and the Effect of Tax Avoidance Using PSO Algorithm

Iranian Journal of Accounting, Auditing and Finance

مقاله 7، دوره 5، شماره 4 - شماره پیاپی 17، اسفند 2021، صفحه 119-134   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/ijaaf.2021.41397

نویسندگان

Hani Gharavi Ahangar^* ؛ Seyedhossein Naslemousavi؛ Ali Akbar Ramezani

Department of Accounting, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

چکیده

Credit ratings reflect the relative ability of companies to meet their financial obligations, the relative default probability, and the recovery probability if the debt is not paid. Credit rating agencies build their information analysis on financial statements, which directly affect the credit rating. Tax activities, meanwhile, may contain useful information for credit rating agencies due to their essential role in influencing corporate credit. Thus, the study aims to investigate corporate tax avoidance's effect on credit rating using the Particle swarm Optimization (PSO) algorithm. Therefore, to achieve the research goal, 101 sample companies were collected in 9 years from 2011 to 2019. The emerging-market scoring model measured credit rating and tax avoidance using two scales of tax-book difference and effective tax rate. The Statistical test related to the results indicates relationships. It is significant between tax avoidance and credit rating.

کلیدواژه‌ها

Credit Ranking؛ Tax Avoidance؛ PSO Algorithm

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