Assessing the Level of tax avoidance in the Face of Establishing Internal Control Requirements With a Comparative Perspective Using the Method of Particle Aggregation Algorithm and Genetics
Subject Areas : مدیریتakbar davaran 1 , kumars biglar 2 , Mehdi Beshkooh 3
1 - PhD Student, Accounting Department, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Assistant Professor, Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran (Corresponding Author)
3 - Assistant Professor, Accounting Department, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Keywords: tax avoidance, Internal Control Requirements , Particle Aggregation Algorithm, Genetic Algorithm,
Abstract :
Internal controls are a determinant of tax avoidance and can prevent management from engaging in tax avoidance and encouraging him to create tax programs appropriate to the applicable regulations and not harm the company in the future. Thus, the purpose of this research is to measure the level of tax avoidance in the face of internal control requirements with a comparative perspective using the method of particle aggregation algorithm and genetics. Therefore, to achieve the goal of the research, samples were collected with 101 companies in a 14-year period from 2006 to 2019. the research questions were analyzed using the particle aggregation optimization algorithm (PSO) and genetics. The statistical test related to the results indicates that there is difference in the tax avoidance before and after the imposition of internal control requirements.Internal controls are a determinant of tax avoidance and can prevent management from engaging in tax avoidance and encouraging him to create tax programs appropriate to the applicable regulations and not harm the company in the future. Thus, the purpose of this research is to measure the level of tax avoidance in the face of internal control requirements with a comparative perspective using the method of particle aggregation algorithm and genetics. Therefore, to achieve the goal of the research, samples were collected with 101 companies in a 14-year period from 2006 to 2019
