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Bakhshi, Ali, Yazdani, Shohreh, Khanmohammadi, Mohammadhamed, Maleki, Ali. (1401). Selection of optimal method to predict report type of independent auditor: Comparison of two approaches of support vector machine and neural network. نشریه تخصصی هنرهای کاربردی, 14(1), 1717-1725. doi: 10.22075/ijnaa.2022.26506.3336
Ali Bakhshi; Shohreh Yazdani; Mohammadhamed Khanmohammadi; Ali Maleki. "Selection of optimal method to predict report type of independent auditor: Comparison of two approaches of support vector machine and neural network". نشریه تخصصی هنرهای کاربردی, 14, 1, 1401, 1717-1725. doi: 10.22075/ijnaa.2022.26506.3336
Bakhshi, Ali, Yazdani, Shohreh, Khanmohammadi, Mohammadhamed, Maleki, Ali. (1401). 'Selection of optimal method to predict report type of independent auditor: Comparison of two approaches of support vector machine and neural network', نشریه تخصصی هنرهای کاربردی, 14(1), pp. 1717-1725. doi: 10.22075/ijnaa.2022.26506.3336
Bakhshi, Ali, Yazdani, Shohreh, Khanmohammadi, Mohammadhamed, Maleki, Ali. Selection of optimal method to predict report type of independent auditor: Comparison of two approaches of support vector machine and neural network. نشریه تخصصی هنرهای کاربردی, 1401; 14(1): 1717-1725. 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

International Journal of Nonlinear Analysis and Applications
مقاله 132، دوره 14، شماره 1، فروردین 2023، صفحه 1717-1725    اصل مقاله (1015.83 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.26506.3336
نویسندگان
Ali Bakhshi1؛ Shohreh Yazdani* 1؛ Mohammadhamed Khanmohammadi1؛ Ali Maleki2
1Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran
2Department of Statistics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
تاریخ دریافت: 14 دی 1400،  تاریخ بازنگری: 15 اسفند 1400،  تاریخ پذیرش: 17 اسفند 1400 
چکیده
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.
کلیدواژه‌ها
Auditor report type؛ support vector machine؛ neural network
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