Abstract
<p>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.</p>
Highlights
Financial prediction of firms is the issue that for several years has been of the interest of many researchers
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
The importance of offering high quality information with a view on recent events in the firms becomes clear; audit reports can prevent these events by creating certain signals
Summary
Financial prediction of firms is the issue that for several years has been of the interest of many researchers. Recent studies on Artificial Neural Networks (ANN) have proved that ANNs are powerful tools for the identification and classification of patterns because of their non-linear, non-parametric, and comparative learning properties. Neural networks have been adopted in different commercial uses such as prediction of financial failure, credit rating, bond rating, prediction of future prices, and financial statement analysis. Despite the vast use of neural networks in different commercial settings, the bulk of literature in the field has been devoted to financial failures. The common point of all studies done on the use of ANN in the predictions is the issue of teaching neural networks, where all researchers have used Back-Propagation (BP) algorithms (Yang et al, 199)
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