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A Machine Learning Approach to Assessing Audit Quality (AQ) in Company with Non-Switching Auditors: Extra Trees Classifier (ETC) Model | ||
| Interdisciplinary Journal of Management Studies | ||
| دوره 19، شماره 1، فروردین 2026، صفحه 121-135 اصل مقاله (822.51 K) | ||
| 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 (AQ) in Tehran stock exchange. This study aims to understand the impact of auditor switching on audit quality and employs adjusted restatements of financial statements (AudFailA, AudFailB) and a new modified report (NMR) as proxies to measure 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. | ||
| کلیدواژهها | ||
| Audit quality؛ Machine learning؛ Non-switching firms؛ Extra trees classifier model؛ Ensemble methods | ||
| مراجع | ||
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