Selection of optimal method to predict report type of independent auditor: Comparison of two approaches of support vector machine and neural network

Investors, creditors, government and other users of financial statements rely on financial information given by the managers of firms to make logical and reasonable decisions. In many cases, the purposes of providers are contradictory to the users’ ones. Therefore, auditing is a tool to enhance the reliability of financial statements presented by firms. In the current research, the selection of an optimal method to predict the report type of independent auditor has been addressed and two approaches of vector machine and neural network have been compared. It was conducted during ۲۰۰۸-۲۰۱۷. ۸۴ firms were reviewed. To train and test the research variables, Voka software has been implemented. The dependent variable is the report type of auditor. Results indicated that the accuracy of the support vector machine algorithm was computed as ۶۶.۱۳% and ۵۶.۷۴% for the training and testing sections, respectively. As well, the accuracy of the neural network model was ۶۱.۲۴% and ۵۵.۰۲% in the training and testing sections, respectively. The support vector machine model was more effective than the neural network.