• Title of article

    Predicting Auditor Opinion by a new Metaheuristic Algorithm: Water Cycle Algorithm

  • Author/Authors

    Moradi ، Mohammad Department of Accounting - College of Management - University of Tehran , Eskandar ، Hoda Department of Accounting - Faculty of Management and Accounting - Allameh Tabataba i University , Yazdifar ، Hassan Department of Accounting - College of Business - University of Derby , Seyedi ، Aziz Department of Mechanical Engineering - University of Semnan , Eskandar ، Hadi Department of Accounting - College of Management - University of Tehran

  • From page
    1189
  • To page
    1202
  • Abstract
    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.
  • Keywords
    Audit Opinion , water cycle algorithm , Logistic regression
  • Journal title
    Iranian Journal of Management Studies (IJMS)
  • Journal title
    Iranian Journal of Management Studies (IJMS)
  • Record number

    2772056