Using the Beneish M-score model: Evidence from non-financial companies listed on the Warsaw Stock Exchange
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DOIhttp://dx.doi.org/10.21511/imfi.17(4).2020.33
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Article InfoVolume 17 2020, Issue #4, pp. 389-401
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The risk of distortion of financial statements has been growing. Following the 2008 crisis, recipients of financial information are increasingly focusing on the likelihood of financial statements being distorted through fraudulent presentation of financial information. Therefore, scientific research pays more attention to models capable of detecting financial statement manipulation.
The paper aims to present the principles of functioning and the possibility of using the Beneish M-score model in Polish realities. It analyzes the history of more than 30 companies listed on the Warsaw Stock Exchange to select those whose history indicates that they can be classified as manipulators, and to select the same number of companies from the control group that are considered as non-manipulators.
The research method involves the analysis of empirical data on companies listed on the Warsaw Stock Exchange.
The analysis showed the 8-factor Beneish model identified manipulators with 100% accuracy and succeeded in identifying non-manipulators. The effectiveness of the 5-factor model was much lower.
To serve the purpose of the study, the effectiveness of the Beneish model was tested on a small sample of Polish listed companies as an introduction to a planned larger scale research. The results obtained are consistent with the results of numerous studies by authors from various countries and confirm the effectiveness of the Beneish model in detecting financial statement manipulation.
Acknowledgment
The publication is sponsored by funds from the Cracow University of Economics for the maintenance and development of research potential.
- Keywords
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JEL Classification (Paper profile tab)M41, M42
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References32
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Tables7
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Figures2
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- Figure 1. Economic fraud frequency from 2010 to 2018
- Figure 2. Losses due to economic fraud over the period 2010–2018
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- Table 1. The frequency of the two types of fraud and the average losses caused by them in the USA in 1996, 2002, 2010 and 2018
- Table 2. Structure of companies tested with the Beneish model
- Table 3. Values of indicators from the Beneish model and the number of red flags in the analyzed companies calculated for financial statements prepared for 2010
- Table 4. Number of “red flags” in companies tested with the Beneish model
- Table 5. Aggregate values using 5-factor and 8-factor models for the analyzed companies
- Table 6. Number of indications of the Beneish 5-factor model for “manipulator” and “non-manipulator” companies
- Table 7. Number of indications of the Beneish 8-factor model for “manipulator” and “non-manipulator” companies
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- Anh, N. H., & Linh, N. H. (2016). Using the M-score Model in Detecting Earnings Management: Evidence from Non-Financial Vietnamese Listed Companies VNU. Journal of Science: Economics and Business, 32(2), 14-23.
- Association of Certified Fraud Examiners (1993). Cooking the Books. What Every Accountant Should know about Fraud. Austin: ACFE.
- Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.
- Beneish, M. D., Lee, C. M. C., & Nichols, D. C. (2002). Earnings Manipulation and Expected Returns. Financial Analysts Journal, 69(2), 57-82.
- Beneish, M. D (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16(3), 271-309.
- Comiskey, E. E., & Mulford Ch. W. (2002. The Financial Numbers Game. Detecting Creative Accounting Practices. New York: John Wiley & Sons.
- Comporek, M. (2018). Możliwości i ograniczenia wykorzystania modeli memoriałowych korekt zysku netto w detekcji zarządzania zyskiem. Zeszyty Teoretyczne Rachunkowości, 100(156), 49-66.
- De Angelo, L. (1986). Accounting numbers as market valuation substitutes: a study of management buyouts of public stockholders. The Accounting Review, 61(3), 400-420.
- Dechow, P. M., & Dichev, I. (2002). The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review, 77(Supplement), 35-59.
- Dechow, P. M., Richardson, S. A., & Tuna I. (2003). Why are earnings kinky? An examination of the earnings management explanation. Review of Accounting Studies, 8(2-3), 355-384.
- Dechow, P. M., Slowan, R. G., Sweeney, A. P. (1995). Detecting Earnings Management. The Accounting Review, 70(2), 193-225.
- Fen-May, L. (2008). Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Accounting Journal, 23(7), 650-662.
- Glancy, F. H., & Yadav, S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50(3), 595-601.
- Healy, P. M. (1985). The Effect of Bonus Schemes on Accounting Decisions. Journal of Accounting and Economics, 7(1-3), 85-107.
- Hepworth, S. R. (1953). Smoothing Periodic Income. The Accounting Review, 28(1). 32-39.
- Herawati, N. (2015). Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia – Social and Behavioral Sciences, 211(25), 924-930.
- Hołda, A., Kutera, M., & Surdykowska S. (2006). Oszustwa finansowe. Warsaw: Difin.
- Jones, J. (1991). Earnings Management during Import Relief Investigations. Journal of Accounting Research, 29(2), 193-228.
- Kaminski, K. A., Wetzel, T. S., & Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19(1), 15-28.
- Kara, E., Korpi, M., & Ugurlu, M. (2015). Using Beneish model in identifying accounting manipulation: an empirical study in BIST manufacturing industry sector. Journal of Accounting, Finance and Auditing Studies, 1(1), 21-39.
- Kaur, R., Sharma, K., & Khanna, A. (2014). Detecting Earnings Management in India – A sectorwise Study on European. Journal of Business and Management, 6(11), 11-18.
- Kothari, S. P., Leone, A., & Wasley, C. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197.
- MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score Models to Detect Financial Fraud and Corporate Failure: A Case Study of Enron Corporation. International Journal of Finance and Accounting, 6(6), 159-166.
- Magrath, L., & Weld, L. G. (2002). Abusive earnings management and early warnings signs. The CPA Journal, 72(8), 51-54.
- Mahama, M. (2015). Detecting corporate fraud and financial distress using the Altman and Beneish models. International Journal of Economics, Commerce and Management, 3(1), 1-18.
- Mcnichols, M. (2002). Discussion of the quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review, 77(Supplement), 61-69.
- Omar, N., Koya, R. K., Sanusi, Z. M., & Shafe, N. A. (2014). Financial statement fraud: A Case examination using Beneish model and ratio analysis. International Journal of Trade, Economics and Finance, 5(2), 184-186.
- Paolone, F., & Magazzino, C. (2014). Earnings manipulation among the main industrial sectors: Evidence from Italy. Economia Aziendale, 5, 253-261.
- Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063-1073.
- Ronen, J., & Yaari, V. (2008). Earnings management. New York: Springer.
- Staszel, A. (2019). Obszar swobody w rachunkowośc. Warsaw: Difin.
- Zack, G. M. (2009). Fair Value Accounting Fraud New Global Risks and Detection Techniques. New Jersey: John Wiley & Sons.