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Abdi, Mostafa, Moslemi, Azar, Rashidi, Mohsen. (1404). A Machine Learning Approach to Assessing Audit Quality in Company with Non-Switching Auditors: Extra Trees Classifier Model. , (), -. doi: 10.22059/ijms.2025.384690.677133
Mostafa Abdi; Azar Moslemi; Mohsen Rashidi. "A Machine Learning Approach to Assessing Audit Quality in Company with Non-Switching Auditors: Extra Trees Classifier Model". , , , 1404, -. doi: 10.22059/ijms.2025.384690.677133
Abdi, Mostafa, Moslemi, Azar, Rashidi, Mohsen. (1404). 'A Machine Learning Approach to Assessing Audit Quality in Company with Non-Switching Auditors: Extra Trees Classifier Model', , (), pp. -. doi: 10.22059/ijms.2025.384690.677133
Abdi, Mostafa, Moslemi, Azar, Rashidi, Mohsen. A Machine Learning Approach to Assessing Audit Quality in Company with Non-Switching Auditors: Extra Trees Classifier Model. , 1404; (): -. doi: 10.22059/ijms.2025.384690.677133

A Machine Learning Approach to Assessing Audit Quality in Company with Non-Switching Auditors: Extra Trees Classifier Model

Interdisciplinary Journal of Management Studies
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 07 تیر 1404
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijms.2025.384690.677133
نویسندگان
Mostafa Abdi1؛ Azar Moslemi* 1؛ Mohsen Rashidi2
1Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran.
2Department of Economic and Administration Science Faculty, Lorestan University, Lorestan, Iran.
چکیده
In this study, the authors utilize machine learning techniques to investigate the likelihood of a company switching auditors and examine whether the increased likelihood of switching is associated with audit quality in Tehran stock exchange (TSE). This study aims to understand the impact of auditor switching on audit quality and uses adjusted restatements of financial statements (AudFailA and AudFailB) and a new modified report (NMR) as proxies to measure adjusted audit quality, based on the environmental conditions of the research. These findings indicate that companies with a higher likelihood of switching auditors but ultimately deciding to stay with incumbent auditors exhibit poor audit quality. It is worth noting that this study focuses on larger companies, and the results show a stronger association between audit quality and the probability of switching auditors in this group. This finding suggests that the relationship between audit quality and the decision to switch auditors is more significant among larger companies than among smaller ones. The methodology employed in this study is designed to be implemented by investors, audit firms, and regulators. It aims to identify companies with a higher probability of switching auditors, allowing for proactive measures to address the potential deterioration in audit quality.
کلیدواژه‌ها
Audit Quality؛ Machine Learning؛ Auditor switch؛ Non-Switching Firms؛ Extra Trees Classifier model
آمار
تعداد مشاهده مقاله: 3





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