A Machine Learning Approach to Assessing Audit Quality (AQ) in Company with Non-Switching Auditors: Extra Trees Classifier (ETC) Model
نویسندگان
1 Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran
2 Department of Economic and Administration Science Faculty, Lorestan University, Lorestan, Iran
3 Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran
doi
10.22059/ijms.2025.384690.677133چکیده
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.
