The Investigation and Comparison of the Performance of Heuristic Methods in the Prediction of the Type of Auditor’s Opinion in Firms Accepted in Tehran Stock Exchange

Nasim Nasirpour, Alireza Mazdaki and Esmail Enayati

Asian Social Science, 2016, vol. 12, issue 6, 148

Abstract: Stock companies play a key role in the economy of any country and the success of these companies depends to a great degree on investors and creditors’ interest who invest in them. Auditors’ reports assume a special position in the decisions taken by investors and creditors. Therefore, the importance of offering high quality information with a view on recent events in the firms (bankruptcy and dissolution, financial scandals, loses suffered by creditors, etc.) becomes clear; moreover, audit reports can prevent these events by creating certain signals. To this end, modern heuristic methods for the prediction of the type of auditor’s opinion are offered in this paper. The aim of this study is to investigate the ability of probabilistic neural network method and to compare it with artificial neural network in order to identify and predict the type of independent auditor’s opinion in Iran in the time period of 2009 to 2013. The patterns used to predict the type of independent auditor’s opinion can be divided into different categories-these categories are becoming more complex and more advanced- single-variable models, multi discriminant analysis, regression function, neural networks, etc. neural networks are getting increasing popularity among researchers for their non-linear and non-parametric properties. Therefore, modern approaches are used in this study to predict the type of auditor’s opinion.

Date: 2016
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