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Predicting Audit Opinion by a new Metaheuristic Algorithm: Water Cycle Algorithm | ||
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 دی 1402 | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijms.2023.362553.676054 | ||
نویسندگان | ||
Hoda Eskandar* 1؛ mohammad moradi2؛ Hassan Yazdifar3؛ Hadi Eskandar4؛ Aziz Seyedi5 | ||
1Assistant Professor of Accounting, management and accounting faculty, Allameh Tabataba'i University | ||
2Associate Prof., Department of Accounting, Management faculty, University of Tehran | ||
3Prof. of Accounting, Derby University | ||
4M.S., Faculty of mechanical Engineering, University of semnan | ||
5PHD. of Accounting, Management faculty, University of Tehran | ||
چکیده | ||
An auditor evaluates if financial statements which the firms issue in public, present fairly. The audit report is a formal letter containing independent verification of the quality of financial statements used for making economic decisions. Hence, the issuance of such a report transmits some news about the firm and enhances confidence degree in the financial statements. This study predicts audit opinion of the firms listed in Tehran Stock Exchange during 2018-2020 by a new metaheuristic algorithm named Water Cycle Algorithm (WCA) and compares its results with one of the most popular methods called logistic regression (LG). 24 variables were extracted from the literature and used for this prediction. 4 evaluating criteria were used to compare the predictions of two methods. According to findings, the superiority of the criteria in the WCA was confirmed in comparison to LG. Since WCA was more appropriate, users of financial reports can use it for forcasting audit judgment in interim statements, and auditors can utilize it as well, evaluating and accepting clients and achieving an acceptable level of audit risk, as a quality control tool. | ||
کلیدواژهها | ||
Audit Opinion؛ Water Cycle Algorithm؛ Logistic Regression | ||
آمار تعداد مشاهده مقاله: 47 |