ISSN : 1738-3110
Purpose - The purpose of this study was to predict the selection of independent auditors in the companies listed on the Tehran Stock Exchange (TSE) using a combination of PSO algorithm and CART. This study involves applied research. Design, approach and methodology - The population consisted of all the companies listed on TSE during the period 2005-2010, and the sample included 576 data specimens from 95 companies during six consecutive years. The independent variables in the study were the financial ratios of the sample companies, which were analyzed using two data mining techniques, namely, PSO algorithm and CART. Results - The results of this study showed that among the analyzed variables, total assets, current assets, audit fee, working capital, current ratio, debt ratio, solvency ratio, turnover, and capital were predictors of independent auditor selection. Conclusion - The current study is practically the first to focus on this topic in the specific context of Iran. In this regard, the study may be valuable for application in developing countries.
Alpaydin, E. (2010). Introduction to Machine Learning (2nd Edition). London, England: MIT Press.
Breiman, L.,Friedman, J., Stone C.J., & Olshen R.A. (1984). Classification and Regression Trees (1st Edition). New York: Chapman and Hall/CRC.
Carvalho, M., & Ludermir, T.B. (2006). An analysis of PSO hybrid algorithms for feed-forward neural networks training. Proceedings of the Ninth Brazilian Symposium on Neural Networks (SBRN'06).
Chaney, P., Jeter, D., &Shivakumar, L. (2004). Self-selection of auditors and audit pricing in private firms. The Accounting Review, 79(1), 51-72.
Cravens, K.S., Flagg, J.C., & Glover, H.D. (1994). A comparison of client characteristics by auditor attributes: Implications for the auditor selection process. Managerial Auditing Journal, 9(3), 27-36.
Gupta, M.M., Jin, L., & Homma, N. (2003). Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory (1st Edition). New Jersey: Wiley-IEEE Press.
Kane, G.D., & Velury, U. (2004). The role of institutional ownership in the market for auditing services: An empirical investigation. Journal of Business Research, 57(9), 976-983.
Khanesar, M.A., Teshnehlab, M., & Shoorehdeli, M.A. (2007). A novel binary particle swarm optimization. Proceedings of the Mediterranean Conference of MED'07 (pp1-6). Control and Automation.
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2008). Support vector machines, decision trees and neural networks for auditor selection. Journal of Computational Methods in Science and Engineering, 8(3), 213-224.
Meigs, W.B., Whittington, O.R., Pany, K., & Meigs, R.F. (1992). Principles of Auditing (10th Edition). Boston: Irwin Professional Publishing.
Mitchell, T.M. (1997). Machine Learning. New York, NY :McGraw-Hill.
Nourvash, I., Mehrani, S., Karami, G.R., & Shahbazi, M. (2011). A Comprehensive Review of Auditing. Tehran, Iran:Negahe Danesh.
Quinlan, J.R. (1993). C4.5:Programs for Machine Learning (1st Edition). San Francisco: Morgan Kaufmann.
Soke, A., & Bingul, Z. (2007). Applications of discrete PSO algorithm to two-dimensional non-guillotine rectangular packing problems. Proceedings of GEM (pp. 127-132), GEM.
Velury,U., Reisch, J.T., & O’Reilly, D.M. (2003). Institutional ownership and the selection of industry specialist auditors. Review of Qualitative Finance and Accounting, 21, 35-48.
Walsh, T.J., Garden, J.W., & Gallagher, B. (1969). Obliteration of retinal venous pulsations during elevation of cerebrospinal-fluid pressure. American Journal of Ophthalmology, 67(6), 954-956.