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

Bakhshi, Ali; Yazdani, Shohreh; Khanmohammadi, Mohammadhamed; Maleki, Ali

doi:10.22075/ijnaa.2022.26506.3336

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

Document Type : Research Paper

Authors

Ali Bakhshi ¹

Shohreh Yazdani ¹

Mohammadhamed Khanmohammadi ¹

Ali Maleki ²

¹ Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran

² Department of Statistics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

10.22075/ijnaa.2022.26506.3336

Abstract

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 2008-2017. 84 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 66.13% and 56.74% for the training and testing sections, respectively. As well, the accuracy of the neural network model was 61.24% and 55.02% in the training and testing sections, respectively. The support vector machine model was more effective than the neural network.

Keywords

Auditor report type

support vector machine

neural network