International Journal of Nonlinear Analysis and Applications
Explaining the Beneish model and providing a comprehensive model of fraudulent financial reporting(FFR)
Document Type : Research Paper
Authors
1 Ph.D. Student of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 Assistant prof. of Accounting, kangavar Branch, Islamic Azad University, kangavar,Iran.
3 Assistant prof. of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah,Iran.
Abstract
In this study, the aim is to provide a comprehensive model for the prediction, prevention and detection of financial reporting fraud using the modified benchmarking model. To achieve the research goal, the necessary data were collected for 161 companies listed on the Tehran Stock Exchange during a 10-year period (2009-2018). The results of estimating the research model have been examined by the binomial logit method. The results of testing the hypotheses of this study indicate that Beneish model is successful in separating companies involved in fraudulent financial reporting and healthy companies, based on McFadden's detection coefficient, with 73% confidence, and among the independent variables, day’s sales in receivable index (DSRI), gross margin index (GMI), asset quality index (AQI), sales growth index (SGI), depreciation index (DEPI) and total accrual to total assets index (TATAI), have a direct and significant effect on fraudulent financial reporting, but sales, general, and administrative expenses index (SGAI) and leverage index (LEVI) have had a significant inverse effect on fraudulent financial reporting (FFR).
Keywords
Neural Network and Particle Swarm Optimization Algorithm for Earnings Management Prediction. Accounting
and Auditing Reviews, (2019), 615-638. (in persian).
[2] NA. Aris, SM. Arif, R. Othman, Zain. Mohamed, Fraudulent Financial Statement Detection Using Statistical
Techniques: The Case Of Small Medium Automotive Enterprise. The Journal of Applied Business Research, Vol.
31 No. 4, (2015), 1469-1478.
[3] W. A. Asyiqin, Disclosure of corporate governance structure and the likelihood of fraudulent financial reporting.
Procedia-Social and Behavioral Sciences 145, (2014), 243-253.
[4] B. H. Baltagi , Econometric Analysis of panel data, 5th Edition, John Wielyand Sons, (2013), 190-195.
[5] M. D. Beneish, The Detection of Earnings Manipulation. Financial Analysts Journal, 55 (5),(1999), 24-36.
[6] A. Darzi Ramandi, Investigating the effect of fraudulent financial reporting on the risk of stock price falls with
emphasis on managerial ownership. Journal of Accounting and Management, Volume 1, Number 1, (2018), 14-28.
(in persian).
[7] H. Etemadi,H. Zalaghi, Logistic Regression In Detecting of Fraudmental Financial Statements with Using Financial Ratios. Journal of Audit Science, Thirteenth Year, No. 51, Summer, (2013). (in persian).
[8] H. Farajzadeh Dehkordi, L. Aghaei,Policy of Profit Distribution and Fraudulent Financial Reporting. Quarterly
Journal of Experimental Studies in Financial Accounting Studies, Volume 12, Number 45, Spring 2015, (2015),
97-114. (in persian).
[9] Sh. Khajavi,Sh. Mansouri, Hidden Circle in the Financial Reporting Security Chain. Official Accountant Quarterly, No. 28, (2015), 64-54. (in persian).
[10] Sh. Khajavi, M. Ebrahimi, Presenting a New Computational Approach to Predicting Fraud in Financial Statements, Using Clustering and Classification Methods. Journal of Accounting Advances, Shiraz University, Volume
9, Number 2, Fall and Winter, (2017), 1-34. (in persian).[11] M. Kusumasanthi, T. Kartika Hendra, Wijayanti. Anita, Detection Financial Statement Fraud with Beneish
Model: An Empirical In Indonesia. The 2nd International Conference on Technology, Education, and Social
Science 2018 (The 2nd ICTESS 2018), (2018), 94-95.
[12] J. Moradi,R. Rostami, R. Zare, Identifying risk factors affecting the probability of fraud in financial reporting
from the auditors’ point of view and examining their impact on the company’s financial performance. Shiraz
University Accounting Advances Journal, Volume 6, Issue 1, Spring And Summer,(2014), 141-173. (in persian).
[13] O. Pourheidari,N. Samieinejad,Investigating the Impact of Corporate Governance Structure on Financial Reporting. Auditing Organization, Applied Research in Financial Reporting, Fourth Year, No. 6, Spring and Summer,
(2015), 53-80. (in persian).
[14] N. Rahimiyan, R. Haji Heydari, Investigation of fraud detection using a modified model of cash and financial
ratios. Empirical Accounting Research, Year 8, Issue 31, Spring 2019,(2016), 47-69. (in persian).
[15] S. Reposis,Using beneish model to detect corporate financial statement fraud Greece. Journal of Financial Crime,
23(4),(2016), 1063-1073.
[16] H. Sajjadi, T. Kazemi, Comprehensive Model of Fraudulent Financial Reporting in Iran, Using Fundamental
Theorizing Methodology. Experimental Accounting Research, Volume 6, Number 3, Fall 2016,(2016), 185-204.
(in persian).
[17] L. Sedighi Kamal, Fraud in financial statements based on the report of the Association of Official Fraud Inspectors.
Auditor, No. 64, (2013), 120-116. (in persian).
[18] S. Shaari Anaghiz, N. Rahimian, J. Salehi Sedghiani, Aboutaleb Khorasani, Investigating and Implementing
the Accuracy of the Results of Beneish and Modified Beneish Models Based on Iran’s Economic Environment,
in Detecting and Revealing Fraudulent Financial Reporting. Financial Management Perspective, No. 18,(2017),
105-123. (in persian).
[19] I. Sony Agus, Gh. Imam,F. Faisal, P. Imang Dapit,Detection Fraudulent Financial Statement: Beneish M-Score
Model. Wseas Transactions on Business and Economics,(2019), 271-281.
[20] E. Vahidi Elizaei,H. Hamedian, Iranian auditors’ perception of the effectiveness of risk signs in detecting fraudulent
financial reporting. .Accounting Research, No. 3, (2009), 162-197. (in persian).
[21] Wang. Zhi, Qi. Zheng, Journal of Accounting and Public Policy, Volume 36, Issue 2,(2017), Pages 141-162.
Received from: https://www.sciencedirect.com/science/article/pii/S0278425417300170
)
Volume 12, Special Issue
December 2021Pages 39-48
- Receive Date: 07 October 2020
- Revise Date: 27 December 2020
- Accept Date: 18 January 2021