A Machine Learning Approach to Assessing Audit Quality (AQ) in Company with Non-Switching Auditors: Extra Trees Classifier (ETC) Model

Document Type : Research Paper

Authors

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

2 Department of Economic and Administration Science Faculty, Lorestan University, Lorestan, Iran

Abstract

In this study, the authors utilize machine learning techniques to investigate the likelihood of a company switching auditors and examine whether the increased likelihood of switching is associated with audit quality (AQ) in Tehran stock exchange. This study aims to understand the impact of auditor switching on audit quality and employs adjusted restatements of financial statements (AudFailA, AudFailB) and a new modified report (NMR) as proxies to measure audit quality, based on the environmental conditions of the research. These findings indicate that companies with a higher likelihood of switching auditors, but ultimately deciding to stay with incumbent auditors, exhibit poor audit quality.

Keywords

Main Subjects

References

Aghaei Chadegani, A., Mohamed, Z., & Jari, A. (2013). The determinant factors of auditor switch among companies listed on tehran stock exchange. International Research Journal of Finance and Economics, 80(80), 158-168. https://ssrn.com/abstract=2227597
Aobdia, D. (2016). The validity of publicly available measures of audit quality: Evidence from the PCAOB inspection data. Social Science Research Network (SSRN). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2629305.
Aobdia, D. (2019). Do practitioner assessments agree with academic proxies for audit quality? Evidence from PCAOB and internal inspections. Journal of Accounting and Economics, 67(1), 144-174. https://doi.org/10.1016/j.jacceco.2018.09.001
Ashbaugh-Skaife, H., Collins, D. W., & Kinney Jr, W. R. (2007). The discovery and reporting of internal control deficiencies prior to SOX-mandated audits. Journal of Accounting and Economics, 44(1-2), 166-192. https://doi.org/10.1016/j.jacceco.2006.10.001
Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235. https://doi.org/10.1111/1475-679X.12292
Barnes, P., & Renart, M. A. (2013). Auditor independence and auditor bargaining power: Some Spanish evidence concerning audit error in the going concern decision. International Journal of Auditing, 17(3), 265-287. https://doi.org/10.1111/ijau.12003
Beardsley, E. L., Imdieke, A. J., & Omer, T. C. (2021). The distraction effect of non-audit services on audit quality. Journal of Accounting and Economics, 71(2), 101380. https://doi.org/https://doi.org/10.1016/j.jacceco.2020.101380 https://doi.org/10.1016/j.jacceco.2020.101380
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2020). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468-519. https://doi.org/10.1007/s11142-020-09563-8
Public Company Accounting Oversight Board (PCAOB). (2013). Improving the transparency of audits: Proposed amendments to PCAOB auditing standards to provide disclosure in the auditor's report of certain participants in the audit. https://pcaobus.org/Standards/Auditing/Pages/default.aspx
Brown, S. V., & Knechel, W. R. (2016). Auditor–client compatibility and audit firm selection. Journal of Accounting Research, 54(3), 725-775. https://doi.org/10.1111/1475-679X.12105
Calocha, R., & Herwiyanti, E. (2020). Factors that affect audit quality. Journal of Contemporary Accounting, 2, 35-48. https://doi.org/10.20885/jca.vol2.iss1.art4
Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146-1160. https://doi.org/10.1287/mnsc.1100.1174
Chen, F., Peng, S., Xue, S., Yang, Z., & Ye, F. (2016). Do audit clients successfully engage in opinion shopping? Partner‐level evidence. Journal of Accounting Research, 54(1), 79-112. https://doi.org/https://doi.org/10.1111/1475-679X.12097
Chen, P. F., He, S., Ma, Z., & Stice, D. E. (2012). Qualified audit opinions and debt contracting. Available at SSRN. https://hdl.handle.net/1783.1/7932
Chung, H., Kim, Y., & Sunwoo, H.-Y. (2021). Korean evidence on auditor switching for opinion shopping and capital market perceptions of audit quality. Asia-Pacific Journal of Accounting & Economics, 28(1), 71-93. https://doi.org/10.1080/16081625.2020.1845000
Chung, H., Sonu, C. H., Zang, Y., & Choi, J.-H. (2019). Opinion shopping to avoid a going concern audit opinion and subsequent audit quality. Auditing: A Journal of Practice & Theory, 38(2), 101-123. https://doi.org/10.2308/ajpt-52154
Commission, E. (2010). Green paper: Audit policy: Lessons from the crisis. Publications Office of the European Union. https://www.europarl.europa.eu/meetdocs/2009_2014/documents/com/com_com(2010)0561_/com_com(2010)0561_en.pdf
Cowle, E. N., Decker, R. P., & Rowe, S. P. (2023). Retain or rotate: The association between frequent auditor switching and audit quality. Accounting Horizons, 1-23. https://doi.org/10.2308/horizons-2022-018
DeFond, M. L., & Subramanyam, K. (1998). Auditor changes and discretionary accruals. Journal of Accounting and Economics, 25(1), 35-67. https://doi.org/10.1016/S0165-4101(98)00018-4
 DeFond, M. L., Raghunandan, K., & Subramanyam, K. R. (2002). Do non–audit service fees impair auditor independence? Evidence from going concern audit opinions. Journal of Accounting Research, 40(4), 1247-1274. SSRN: https://ssrn.com/abstract=297747 or http://dx.doi.org/10.2139/ssrn.297747
DeFond, M., & Zhang, J. (2014). A review of archival auditing research. Journal of Accounting and Economics, 58(2-3), 275-326. https://doi.org/10.1016/j.jacceco.2014.09.002
Gibbins, M., Salterio, S., & Webb, A. (2001). Evidence about auditor–client management negotiation concerning client’s financial reporting. Journal of Accounting Research, 39(3), 535-563. SSRN: https://ssrn.com/abstract=259304 or http://dx.doi.org/10.2139/ssrn.259304
Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing, 16, 14-28. https://www.researchgate.net/publication/245508224_Assessing_the_Risk_of_Management_Fraud_Through_Neural_Network_Technology
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. https://dx.doi.org/10.2139/ssrn.3159577
Gul, F. A., Jaggi, B. L., & Krishnan, G. V. (2007). Auditor independence: Evidence on the joint effects of auditor tenure and nonaudit fees. Auditing: A Journal of Practice & Theory, 26(2), 117-142. https://ssrn.com/abstract=984656
Haghighi, R., Bagherpour Velashani, M. A., Ghanaei Chamanabad, A., & Abbaszadeh, M. R. (2023). Extending and ranking the audit judgment factors in an emerging market with an emphasis on cognitive neuroscience. Iranian Journal of Management Studies (IJMS), 16(2), 549-566.  http//doi.org/10.22059/ijms.2022.338818.674965
Haghighi, R., Bagherpour Velashani, M. A., Ghanaei Chamanabad, A., & Abbaszadeh, M. (2025). Auditors' sensory processing capability and objectivity: Are auditors really objective? Interdisciplinary Journal of Management Studies (IJMS), 18(1), 129-143. http//doi.org/10.22059/ijms.2023.357703.675764
Hall, C., Judd, J. S., & Sunder, J. (2023). Auditor conservatism, audit quality, and real consequences for clients. Review of Accounting Studies, 28(2), 689-725. https://doi.org/10.1007/s11142-021-09653-1
Hu, N., Xu, J., & Xue, S. (2022). Regulatory risk and auditors’ reporting conservatism: Evidence from Chinese comment letters. Journal of Accounting and Public Policy, 41(6), 106997. https://doi.org/10.1016/j.jaccpubpol.2022.106997
Hunt, J. O. S., Rosser, D. M., & Rowe, S. P. (2021). Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients. Journal of Accounting and Public Policy, 40(5), 106785. https://doi.org/10.1016/j.jaccpubpol.2020.106785
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2
Jones, S. (2017). Corporate bankruptcy prediction: A high dimensional analysis. Review of Accounting Studies, 22(3), 1366-1422. https://doi.org/10.1007/s11142-017-9407-1
Kinney Jr, W. R., Palmrose, Z. V., & Scholz, S. (2004). Auditor independence, non‐audit services, and restatements: Was the US government, right? Journal of Accounting Research, 42(3), 561-588. https://doi.org/10.1111/j.1475-679X.2004.t01-1-00141.x
Krishnan, J. (1994). Auditor switching and conservatism. Accounting Review, 200-215. https://www.jstor.org/stable/248267
Lennox, C. (2000). Do companies successfully engage in opinion-shopping? Evidence from the UK. Journal of Accounting and Economics, 29(3), 321-337. https://doi.org/10.1016/S0165-4101(00)00025-2
Lennox, C., & Pratt, K. (2003). Opinion shopping and the role of audit committees when audit firms are dismissed: The US experience. Edinburgh. https://www.researchgate.net/publication/243460073_Opinion_Shopping_and_the_Role_of_Audit_Committees_when_Audit_Firms_are_Dismissed_the_US_Experience
Lennox, C. S., & Park, C. W. (2007). Audit firm appointments, audit firm alumni, and audit committee independence. Contemporary Accounting Research, 24(1), 235-258. https://ssrn.com/abstract=942178
Li, C. (2009). Does client importance affect auditor independence at the office level? Empirical evidence from going‐concern opinions. Contemporary Accounting Research, 26(1), 201-230. https://ssrn.com/abstract=1108189
Lotfi, S., Hajiha, Z., & Vakilifard, H. (2023). An examination of auditor confirmation bias and audit quality with an emphasis on the moderating role of client and auditor characteristics. Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies), 16(1), 183-204. https://doi.org/10.22059/ijms.2022.338312.674940
Lu, T. (2006). Does opinion shopping impair auditor independence and audit quality? Journal of Accounting Research, 44(3), 561-583. https://doi.org/10.1111/j.1475-679X.2006.00211.x
Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. (2002). Credit card fraud detection using Bayesian and neural networks. In Proceedings of the 1st International Naiso Congress on Neuro Fuzzy Technologies (Vol. 261, p. 270). http://como.vub.ac.be/Members/bram.htm
McGrath, S., Siegel, A., Dunfee, T. W., Glazer, A. S., & Jaenicke, H. R. (2001). A framework for auditor independence. Journal of Accountancy, 191(1), 39. https://www.journalofaccountancy.com/issues/2001/jan/aframeworkforauditorindependence/
Metz, C. E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8(4), 283-298. https://doi.org/10.1016/S0001-2998(78)80014-2
Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008
Mohammadrezaei, F., Mohd-Saleh, N., & Ahmed, K. (2018). Audit firm ranking, audit quality and audit fees: Examining conflicting price discrimination views. The International Journal of Accounting, 53(4), 295-313. https://doi.org/10.1016/j.intacc.2018.11.003
Mohammadrezaei, F., & Faraji, O. (2019). The dilemma of audit quality measuring in archival studies: critiques and suggestions for iran’s research setting. Journal of Accounting and Auditing Review, 26(1), 87-122. https://doi.org/10.22059/acctgrev.2018.256781.1007884
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. DOI: 10.1257/jep.31.2.87
Newton, N. J., Persellin, J. S., Wang, D., & Wilkins, M. S. (2015). Internal control opinion shopping and audit market competition. The Accounting Review, 91(2), 603-623. https://doi.org/10.2308/accr-51149
 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830. http://scikit-learn.sourceforge.net/
Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53. https://doi.org/10.2308/accr-51562. https://doi.org/10.1016/j.adiac.2010.10.004
Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92(2), 221-245. https://doi.org/10.2308/accr-51562
Pourheidari, O. & Golmohammadi, M. (2023). Stakeholder management and audit fees. Iranian Journal of Management Studies (IJMS), 16(4), 843-856. http//doi.org/10.22059/ijms.2022.341098.675038
Ruiz-Barbadillo, E., Gómez-Aguilar, N., & Biedma-López, E. (2006). Long-term audit engagements and opinion shopping: Spanish evidence. In Accounting Forum (Vol. 30, No. 1, pp. 61-79). https://doi.org/10.1016/j.accfor.2005.03.007
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x. https://doi.org/10.1007/s42979-021-00592-x
Securities and Exchange Commission. (1988). Disclosure amendments to regulation SK, form 8-k and schedule 14A regarding changes in accountants and potential opinion shopping situations. Financial Reporting Release, (31), 1140-1147. https://www.sec.gov/files/reg-sk-disclosure-requirements-review.pdf
Taqi, M., Kalbuana, N., Abbas, D. S., & Mayyizah, M. (2024). Litigation risk: Delving into audit quality, internal audit structure, political connections, and company size. Interdisciplinary Journal of Management Studies (IJMS), 17(4), 1217-1232. http//doi.org/10.22059/ijms.2024.367759.676318
Tan, C. E., & Young, S. M. (2015). An analysis of “Little r” restatements. Accounting Horizons, 29(3), 667-693. https://doi.org/10.2308/acch-51104
Tsai, C.-F., & Wu, J.-W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639-2649. https://doi.org/10.1016/j.eswa.2007.05.019
Van Brenk, H., Majoor, B., & Wright, A. M. (2021). The effects of profit-sharing plans, client importance, and reinforcement sensitivity on audit quality. Auditing: A Journal of Practice & Theory, 40(1), 107-131. https://doi.org/10.2308/AJPT-18-118
Watkins, A. L., Hillison, W., & Morecroft, S. E. (2004). Audit quality: A synthesis of theory and empirical evidence. Journal of Accounting Literature, 23, 153. https://www.proquest.com/openview/848c22290cbd79f08e07f1a6a491974d/1?pq-origsite=gscholar&cbl=31366
Whiting, D. G., Hansen, J. V., McDonald, J. B., Albrecht, C., & Albrecht, W. S. (2012). Machine learning methods for detecting patterns of management fraud. Computational Intelligence, 28(4), 505-527. https://doi.org/10.1111/j.1467-8640.2012.00425.x
Xie, Z., Cai, C., & Ye, J. (2010). Abnormal audit fees and audit opinion–Further evidence from China's capital market. China Journal of Accounting Research, 3, 51-70. https://doi.org/10.1016/S1755-3091(13)60019-2
Yuejun, T. (2011). Audit fees, motivation of avoiding loss and opinion shopping: Test for moderating effect based on evidences of Chinese stock market from 2001 to 2008. China Finance Review International, 1(3), 241-261. https://doi.org/10.1108/20441391111144103
Interdisciplinary Journal of Management Studies
Volume 19, Issue 1
January 2026
Pages 121-135

Files

Share

How to cite

Statistics