Determination of Audit Fees Using Support Vector Machine: Evidence from the Tehran Stock Market
Subject Areas : • Internal and external auditing and there innovation
Arezoo Memarimoghadam
1
,
Mohammadhamed Khanmohammadi
2
,
Mohammad Hassani
3
1 - Department of Accounting, Islamic Azad University, North Tehran Branch
2 - Associate Professor, Department of Accounting, Islamic Azad University, Damavand Branch
3 - Assistant Professor, Department of Accounting and Auditing, Faculty of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Audit fee, Determination, Tehran Stock Market , SVR,
Abstract :
Objective: This study explores the determination of audit fees (AF) using Support Vector Regression (SVR) among companies listed on the Iranian stock market from 2017 to 2021. It investigates the relationship between financial variables like financial leverage (DA), current assets ratio (CA), quick ratio (QUICK), ASSETS, current ratio to current liabilities (CR), and long-term debt (DE), with AF as the target. Methodology: Data from 60 listed companies during this period, totaling 279 year-observations, are employed. SVR models are trained on this dataset using Google Colab. Results: The SVR model achieves a 90.5% R2 value and a 3.7 Mean Squared Error (MSE) on training data, indicating high explained variance and reasonable error levels. However, on new data, the model's performance diminishes, with an R2 of 67% and an MSE of 8.1, implying reduced accuracy and intermediate predictive accuracy. Innovation: This study advances the understanding of AF determination using SVR, highlighting the importance of considering various financial variables.
1. Akinola, A. O., & Olagunju, A. (2023). Automated Accounting System and External Audit Fees: Empirical Evidence from Nigeria. KIU Interdisciplinary Journal of Humanities and Social Sciences, 4(1), 55-81.
2. Al Ani, M. K., ALshubiri, F., & Al-Shaer, H. (2024). Sustainable products and audit fees: empirical evidence from western European countries. Sustainability Accounting, Management and Policy Journal.
3. Alibabaee, G., & Khanmohammadi, M. (2022). The Study of the Predictive Power of Meta-heuristic Algorithms to Provide a Model for Bankruptcy prediction. International Journal of Finance & Managerial Accounting, 7(26), 33-51.
4. Azizkhani, M., Hossain, S., & Nguyen, M. (2023). Effects of audit committee chair characteristics on auditor choice, audit fee and audit quality. Accounting & Finance.
5. 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.
6. Bisong E (2019) Building machine learning and deep learning models on Google cloud platform (pp. 59–64). Berkeley, CA: Apress.
7. Boynton WC and Johnson RN (2005) Modern auditing: Assurance services and the integrity of financial reporting. John Wiley & Sons.
8. Causholli M, De Martinis M, Hay D, Knechel W (2011) Audit markets, fees and production: Towards an integrated view of empirical audit research. Journal of Accounting Literature, 29, 167–215.
9. Choi, S. U., Lee, K. C., & Na, H. J. (2022). Exploring the deep neural network model’s potential to estimate abnormal audit fees. Management Decision, 60(12), 3304-3323.
10. Duan, H. K., Vasarhelyi, M. A., Codesso, M., & Alzamil, Z. (2023). Enhancing the government accounting information systems using social media information: An application of text mining and machine learning. International Journal of Accounting Information Systems, 48, 100600.
11. Fedyk, A., Hodson, J., Khimich, N., Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938-985.
12. Ghonji Feshki, A., Khanmohammadi, M. H., & Yazdani, S. (2020). Political connection and earnings management methods: evidence from Tehran stock exchange. International Journal of Finance & Managerial Accounting, 5(17), 1-17.
13. Greiner A, Kohlbeck MJ, Smith, TJ (2017) The relationship between aggressive real earnings management and current and future auditfees. Auditing: A Journal of Practice & Theory, 36(1), 85–107.
14. Hay D (2017) Audit fee research on issues related to ethics. Current Issues in Auditing, 11(2), A1–A22.
15. Hunt, E., Hunt, J., Richardson, V. J., & Rosser, D. (2022). Auditor response to estimated misstatement risk: A machine learning approach. Accounting Horizons, 36(1), 111-130.
16. Kanapathippillai, S., Yaftian, A., Mirshekary, S., Sami, H., & Gul, F. A. (2024). Director turnover, board monitoring and audit fees: Some Australian evidence. Pacific-Basin Finance Journal, 83, 102246.
17. Kim, I., Kong, J. H., & Yang, R. (2024). The impact of board reforms on audit fees: International evidence. Journal of Business Finance & Accounting, 51(1-2), 45-83.
18. Kramer O and Kramer O (2016) Scikit-learn. Machine learning for evolution strategies, pp.45–53.
19. Larbi, S. B., Mandzila, E. E. W., Meniaoui, J., & Moor, E. T. (2024). The influence of auditor and auditee on mandatory audit fees in France. GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología, 12(1), 77-102.
20. Ngoc Hung D, Thuy Van VT, Archer L (2023) Factors affecting the quality of financial statements from an audit point of view: A machine learning approach. Cogent Business & Management, 10(1), 2184225.
21. Mohammadi, M., Yazdani, S., & Khanmohammadi, M. (2021). Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm. Advances in Mathematical Finance and Applications, 6(2), 377-392.
22. Moradi, Z., Ghilavi, M., Khan Mohammadi, M. H., & Hajiha, Z. (2021). Application of resource-based view theory in assessing of efficiency of companies accepted in Tehran stock exchange by data envelopment analysis. Advances in Mathematical Finance and Applications, 6(3), 607-629.
23. Pham, O. T. T. (2024). Forecasting audit opinions on financial statements: statistical algorithm or machine learning?. Electronic Journal of Applied Statistical Analysis, 17(1), 133-152.
24. Prabhawa, A. A., & Harymawan, I. (2022). Readability of Financial Footnotes, Audit Fees, and Risk Management Committee. Risks, 10(9), 170.
25. Ramzan, S., & Lokanan, M. (2024). The application of machine learning to study fraud in the accounting literature. Journal of Accounting Literature.
26. Ranta, M., Ylinen, M., & Järvenpää, M. (2023). Machine learning in management accounting research: Literature review and pathways for the future. European Accounting Review, 32(3), 607-636.
27. Rusmanto T, Waworuntu SR (2015) Factors influencing audit fee in Indonesian Publicly Listed Companies applying GCG. Procedia-Social and Behavioral Sciences, 172, 63-67.
28. Saleh MA, Ragab YM (2023) Determining audit fees: evidence from the Egyptian stock market. International Journal of Accounting & Information Management, 31(2), 355-375.
29. Subedi, M. (2024). Principles based accounting standards, audit fees and going concern: evidence using advanced machine learning. International Journal of Accounting & Information Management, 32(2), 308-344.
30. Subedi, M. (2024). Principles based accounting standards, audit fees and going concern: evidence using advanced machine learning. International Journal of Accounting & Information Management, 32(2), 308-344.
31. Sun, Y., Li, J., Lu, M., & Guo, Z. (2024). Study of the Impact of the Big Data Era on Accounting and Auditing. arXiv preprint arXiv:2403.07180.
32. Xue B, O'Sullivan N (2023) The determinants of audit fees in the alternative investment market (Aim) in the UK: Evidence on the impact of risk, corporate governance and auditor size. Journal of International Accounting, Auditing and Taxation, 50, 100523.