تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,112,370 |
تعداد دریافت فایل اصل مقاله | 97,216,141 |
Predicting Auditor Opinion by a new Metaheuristic Algorithm: Water Cycle Algorithm | ||
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
دوره 17، شماره 4، دی 2024، صفحه 1189-1202 اصل مقاله (1.33 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijms.2023.362553.676054 | ||
نویسندگان | ||
Mohammad Moradi1؛ Hoda Eskandar* 2؛ Hassan Yazdifar3؛ Aziz Seyedi1؛ Hadi Eskandar4 | ||
1Department of Accounting, College of Management, University of Tehran, Tehran, Iran | ||
2Department of Accounting, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran, Tehran, Iran | ||
3Department of Accounting, College of Business, University of Derby, Derby, UK | ||
4Department of Mechanical Engineering, University of Semnan, Semnan, Iran | ||
چکیده | ||
An auditor evaluates whether financial statements which the firms issue in public, present a fair view. 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 offers pertinent details about the firm and enhances confidence degree in the financial statements. This study predicts audit opinion of the firms listed on the Tehran Stock Exchange (TSE) during 2018-2020 using 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. Four evaluating criteria were used to compare the predictions of the two methods. According to the findings, the superiority of the criteria in the WCA was confirmed in comparison with LG. Since WCA was more appropriate, users of financial reports can use it to predict audit opinions in interim statements. Auditors can also utilize it for evaluating and accepting clients, thereby achieving an acceptable level of audit risk, as a quality control tool. | ||
کلیدواژهها | ||
Audit Opinion؛ Water Cycle Algorithm؛ Logistic Regression | ||
مراجع | ||
DeAngelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3, 183–199.
Dopuch, N. W., Holthausen R. & Leftwich R. W. (1987). Predicting audit qualifications with financial and market variables. The Accounting Review, 62(3): 431-454.
Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water Cycle Algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110-111, 151-166.
Heng-Shu, T. (2017).Audit opinion of listed companies: A Takagi-Sugeno fuzzy neural network-based study. Journal of Discrete Mathematical Sciences and Cryptography, 20(4), 899–912.
Karami, G., Karimiyan, T., Salati, S.. (2017). Auditor tenure, auditor industry expertise, and audit report lag: Evidences of Iran. Iranian Journal of Management Studies, 10(3): 641-666.
Laitinen, E. K., & Laitinen, T. (1998). Qualified audit reports in Finland: Evidence from large companies. European Accounting Review, 7(4), 639-653.
Lu, B. (2020). Literature Review of Audit Opinion. Modern Economy, 11, 28-36.
Moalla, H. (2017). Audit report qualification/modification: Impact of financial variables in Tunisia. Journal of Accounting in Emerging Economies, 7, 468–485.
Moradi, M., Ali, S., & Eskandar, H. (2017). The application of water cycle algorithm to portfolio selection. Economic Research-Ekonomska Istraživanja, 30(1), 1277–1299.
Pourheydari, O. & Azami, Z. (2011). Predicting auditor’s opinions: A neural networks approach. Journal of Accounting Knowledge, 1(3), 77-97.
Sánchez-Serrano, J. R., Alaminos, D., García-Lagos, F., & Callejón-Gi, A. M. (2020). Predicting audit opinion in consolidated financial statements with artificial neural networks. Mathematics, 8(8), 1288.
Saaydah, M. (2019). Coeporate governance and the modification of audit opinion: A study in the Jordanian market. International jounal of applied research in management and economics, 2(2), 28-46.
Setayesh, M. H., Ebrahimi, F., Seyf, S. M., Sarikhani, M. (2015). Forecasting the type of audit opinions: A data mining approach. Management Accounting, 5(4), 69-82.
Spathis, C., Doumpos, M., & Zopounidis, C. (2003). Using client performance measures to identify pre-engagement factors associated with qualifed audit reports in Greece. The International Journal of Accounting, 38(3), 267–284.
Susanto, Y. K., & Predipta, A. (2017). Coeporate governance and audit decision making. Corporate ownership & control, 15(1-2), 381-386.
Yaşar, A., Yakut, E., & Gutnu, M. M. (2015). Predicting qualified audit opinions using financial ratios: Evidence from the Istanbul Stock Exchange. International Journal of Business and Social Science, 6(8), 57- 67.
Zeng, S., Li, Y. & Li, Y. (2022). Research on audit opinion prediction of listed companies based on sparse principal component analysis and kernel fuzzy clustering algorithm. Mathematical Problems in Engineering, 2022, 1-13. https://doi.org/10.1155/2022/4053916
| ||
آمار تعداد مشاهده مقاله: 218 تعداد دریافت فایل اصل مقاله: 165 |