عرفان صافدل

Abstract:

Predicting the continuation or non-continuation of the relationship between the auditor and the client in the coming years is one of the challenging issues in the field of auditing. In this regard, the purpose of this research is to predict auditor change (non-continuation of relationship with the current client) using a meta-heuristic algorithm (WCA) and comparing the results with logistic regression method. The statistical sample is ۱۸۵ companies listed on the Tehran Stock Exchange, were selected by systematic elimination method from ۲۰۱۷ to ۲۰۲۳, and their information was collected ۱۳ financial and non financial   variables extracted from the literature were used to predict auditor change in this research. Then, using ۴ evaluation criteria, i.e. accuracy, precision, sensitivity and specifity, the results obtained from the water cycle algorithm were compared with the logistic regression results. In almost all implementations and based on all ۴ performance evaluation criteria, the water cycle algorithm is more suitable than logistic regression for predicting auditor change. In general, the criteria of accuracy, precision, sensitivity and specifity in predicting the change of auditor by the water cycle algorithm were ۸۹, ۷۵, ۲ and ۹۹.۹% respectively. While the logistic regression criteria were ۶۷, ۷۴, ۱ and ۹۹.۸% respectively. In addition, based on the output obtained from the ۱۳ input variables for predicting auditor selection, ۱۲ variables effected in this prediction which are: current ratio, working capital, debt ratio, assets ratio, return on assets, earnings quality, firm's size, auditor opinion type, sepration between CEO, management change, accounting conservatism, and firm's competition power. While the audit firm's size variable was not identified as a criterion for predicting auditor selection.

The water cycle algorithm can be useful for predicting auditor change by users and auditing firms. Audit institutions can use this tool to predict the continuation or non-continuation of their relationship with the client in the coming years and better planning to earn money. Audit clients and companies can also use this tool to predict future relationships with auditors and plan and schedule faster for choosing the next auditor.

Keywords: Water Cycle Algorithm, Logistic Regression.