- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
Predicting Audit Reports Using Meta-Heuristic Algorithms
Predicting Audit Reports Using Meta-Heuristic Algorithms
Fatemeh Salehi (Islamic Azad University)
Mostafa Bahrami (Islamic Azad University)
Abstract
Purpose - This study aims to predict the audit reports of listed companies on the Tehran Stock Exchange by using meta-heuristic algorithms. Research design, data, methodology - This applied research aims to predict auditors reports’ using meta-heuristic methods (i.e., neural networks,the ANFIS, and a genetic algorithm). The sample includes all firms listed on the Tehran Stock Exchange. The research covers the seven years between 2005 and 2011. Results - The results show that the ANFIS model using fuzzy clustering and a least-squares back propagation algorithm has the best performance among the tested models, with an error rate of 4% for incorrect predictions and 96% for correct predictions. Conclusion - A decision tree was used with ten independent variables and one dependent variable the less important variables were removed,leaving only those variables with the greatest effect on auditor opinion (i.e., net-profit-to-sales ratio, current ratio, quick ratio, inventory turnover, collection period, and debt coverage ratio).
- keywords
- Audit report, ANFIS, Tehran Stock Exchange
참고문헌
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