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Predicting the manipulation of financial statements of Tehran Stock Exchange companies using the Benish model and Bayesian networks | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 6، دوره 17، شماره 4، تیر 2026، صفحه 63-73 اصل مقاله (641.08 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2025.37001.5410 | ||
| نویسندگان | ||
| Jabber Awad Mezaal1؛ Arezoo Aghaei Chadegani* 2؛ Mohammad Samir Deherieb Al-Robaaiy3؛ Mohammad Alimoradi1 | ||
| 1Department of Accounting, Isfahan (Khorangan) Branch, Islamic Azad University, Isfahan, Iran | ||
| 2Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran | ||
| 3College of Administration and Economics, Department of Accounting, Al-Muthanna University, Samawah, Iraq | ||
| تاریخ دریافت: 08 اسفند 1403، تاریخ بازنگری: 11 اسفند 1403، تاریخ پذیرش: 30 فروردین 1404 | ||
| چکیده | ||
| The increase in manipulation of information and financial statements of companies, as well as the occurrence of fraud and restatement of financial statements, which often lead to the distress and bankruptcy of companies, has raised concerns about the quality of information in financial statements. Given the importance of this issue, discovering or predicting the occurrence of these manipulations and the factors affecting them has always been of interest to researchers, analysts, investors, and managers in companies. Therefore, the purpose of this research is to predict the manipulation of financial statements of companies listed on the Tehran Stock Exchange using the Benish model and the Bayesian network model, as well as to compare the performance of these models in predicting the manipulation of financial statements with each other. This research is applied in terms of purpose, quantitative and post-event in terms of data, and descriptive-correlation in terms of analysis. The statistical population of the research was all companies listed on the Tehran Stock Exchange in the period 2018 to 2022, and the samples were selected using the systematic elimination method. The criterion for selecting companies with financial statement manipulation was that the companies had an unqualified audit opinion with a qualified clause subject to distortion in financial data or the existence of tax disputes with the tax authority according to the income tax reserve note and tax file and the conditional clause of the audit report or the existence of significant annual adjustments and restated financial statements. The research data was collected using library and document mining methods and analyzed using EViews software. The results showed that the Benish model, with an accuracy of 84.26\% and Bayesian networks, with an accuracy of 90\% have the ability to predict financial statement manipulation among companies listed on the Tehran Stock Exchange. Also, according to the research results, the performance of Bayesian networks, which are artificial intelligence models, in predicting financial statement manipulation is better than the performance of the Benish model, which is a linear model. | ||
| کلیدواژهها | ||
| financial statement manipulation؛ Benish model؛ Bayesian network model | ||
| مراجع | ||
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[1] S.O. Act, Sarbanes-oxley act, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1e106d64c637d9c4c3860d6cbde5db7392689d94, Washington DC, 2002. [2] K. Alden, J. Timmis, P.S. Andrews, H. Veiga-Fernandes, and M.C. Coles, Pairing experimentation and computational modeling to understand the role of tissue inducer cells in the development of lymphoid organs, Front. Immunol. 3 (2012), 172. [3] E.I. Altman, Predicting financial distress of companies: revisiting the Z-score and ZETA® models, In Handbook of research methods and applications in empirical finance, Edward Elgar Publishing, 2013, pp. 428–456. [4] C.S. Armstrong, W.R. Guay, and J.P. Weber, The role of information and financial reporting in corporate governance and debt contracting, J. Account. Econ. 50 (2009), no. 2–3, 179–234. [5] M.D. Beneish, The detection of earnings manipulation, Financ. Anal. J. 55 (1999), no. 5, 24–36. [6] N. Burns and S. Kedia, The impact of performance-based compensation on misreporting, J. Financ. Econ. 79 (2006), no. 1, 35–67. [7] A. Daghmechi Firouzjaei, Accountability in financial reporting: detecting fraudulent companies, Master’s Thesis, University of Mazandaran, 2015. [In Persian] [8] M. Erdogan and E.O. Erdogan, Financial statement manipulation: A Beneish model application, S. Grima, E. Boztepe and P.J. Baldacchino (Eds.), Contemporary issues in audit management and forensic accounting, Emerald Publishing Limited, 2020, pp. 173–188. [9] E. Erikson and M. Hamilton, Companies and the rise of economic thought: The institutional foundations of early economics in England, 1550–1720, Amer. J. Soc. 124 (2006), no. 1, 111–149. [10] M. Ezadpour, A.M. Kordi, F. Tavousi, and Z. Heydari Sureshjani, Operating cash flow manipulation and auditor’s opinion: The moderator role of internal control and audit first ranking, Judg. Decision Mak. Account. and Audit. 2 (2013), no. 7, 1–24. [In Persian] [11] K. Farghandoust Haghighi, S.A. Hashemi, and A. Foroughi Dehkordi, Study of the relationship between earnings management and the possibility of fraud in the financial statements of companies listed on the Tehran Stock Exchange, Audit. Knowledge J. 14 (2013), no. 56, 47–68. [12] M. Hoseinalinezhad, S.M.H. Hashemi Kucheksarai, and A. Jafari, Application of genetic algorithm, particle swarm and artificial neural networks in predicting profit manipulation, J. Invest. Knowledge 13 (2024), no. 52, 613–630. [In Persian] [13] M.A. Howe, Management fraud and earnings management: Fraud versus GAAP as a means to increase reported income, Ph.D. Dissertation, University of Connecticut, 1999. [14] E. Hyblova, A. Kolcavova, T. Urbanek and Z. Petrakova, Can information from publicly available sources reveal manipulation of financial statements? case study of Czech and Slovak companies, Sci. Papers Univ. Pardubice. Ser. D, Faculty Econ. Admin. 30 (2022), no. 3. [15] S. Khajavi and M. Ebrahimi, Investigating the effect of corporate governance mechanisms on fraud in financial statements of companies listed on the Tehran Stock Exchange, J. Asset Manag. Financ. 6 (2018), no. 2, 71–84. [In Persian] [16] I. Kononenko, Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition, Current Trends Knowledge Acquis. 8 (1990). [17] G. Koop, D.J. Poirier, and J. Tobias, Semiparametric Bayesian inference in multiple equation models, J. Appl. Econ. 20 (2005), no. 6, 723–747. [18] N. Malekinia, R. Tehrani, A. Alam Tabriz, and M. Fallah Shams, Development of a profit manipulation prediction model using a hybrid neural network and cosmological algorithms, Monetary Econ. 28 (2021), no. 21, 57–86. [In Persian] [19] A. Marais, C. Vermaak, and P. Shewell, Predicting financial statement manipulation in South Africa: A comparison of the Beneish and Dechow models, Cogent. Econ. Financ. 11 (2023), no. 1, 2190215. [20] J.L. Perols and B.A. Lougee, The relation between earnings management and financial statement fraud, Adv. Account. 27 (2011), no. 1, 39–53. [21] K. Pourghadimi, J. Bahri Sales, S. Jabarzadeh Kangarluei, and A. Zavari Rezaei, Presenting an extended model of the Benish model with emphasis on audit quality characteristics using neural networks, vector machines and random forests, Adv. Finance Invest. 3 (2013), no. 6, 1–30. [In Persian] [22] N. Rahimian, Practical Guide to Managing Fraud Risk in Business, First Edition, Itila’at Publications, 2010. [In Persian] [23] N. Rahimian and R. Haji Heydari, Fraud detection using the modified Benish model and financial ratios, Empir. Res. Account. 8 (2019), no. 31, 47–69. [24] L. Sun and P. Shenoy, Using Bayesian networks for bankruptcy prediction, Eur. J. Oper. Res. 180 (2007), no. 2, 738–753. | ||
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