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Journals / Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 12 Issue: 2

Redefining Financial Manipulation Detection: A New Model's Performance Against the M-Score

Emin Çağlak(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0003-2798-7450
Hafize Meder Çakır(PAMUKKALE UNIVERSITY)0000-0002-3438-9611
Pages
—
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the success of the created manipulation prediction model is eighty-three per cent.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Financial manipulation negatively affects market participants.
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Key Findings & Data:Therefore, various techniques are developed to detect manipulation.
💡
Conclusion & Impact:The results show that the success of the created manipulation prediction model is eighty-three per cent.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Financial statements · Financial fraud · USA

Abstract

Financial manipulation negatively affects market participants. Therefore, various techniques are developed to detect manipulation. One of the most widely used techniques in the literature is the M-score. The study investigates whether the M-score is an effective model across different periods and aims to provide perspective to researchers and market players. In this study, we developed a model to detect financial manipulation. We compared our model results with the M-score results. In the study, we first scanned the US Securities and Exchange Commission's "Accounting and Auditing Practice Bulletins" and identified fifty-one companies (red flags) from the reports. Then, we calculated some ratios from the financial reports of these companies (red flags) and companies with control variables and created the prediction model. The results show that the success of the created manipulation prediction model is eighty-three per cent. The M-score results are lower. The model in the study is the most up-to-date model with many explanatory variables to detect financial manipulation. The study showed that established manipulation detection models may lose effectiveness over time, and the effects of the variables in the model may change. Therefore, the study offers a novel perspective to the literature. In future studies, researchers could investigate how imbalances in the proportion of “1” versus “0” observations affect the detection model.

Özet

Financial manipulation negatively affects market participants. Therefore, various techniques are developed to detect manipulation. One of the most widely used techniques in the literature is the M-score. The study investigates whether the M-score is an effective model across different periods and aims to provide perspective to researchers and market players. In this study, we developed a model to detect financial manipulation. We compared our model results with the M-score results. In the study, we first scanned the US Securities and Exchange Commission's "Accounting and Auditing Practice Bulletins" and identified fifty-one companies (red flags) from the reports. Then, we calculated some ratios from the financial reports of these companies (red flags) and companies with control variables and created the prediction model. The results show that the success of the created manipulation prediction model is eighty-three per cent. The M-score results are lower. The model in the study is the most up-to-date model with many explanatory variables to detect financial manipulation. The study showed that established manipulation detection models may lose effectiveness over time, and the effects of the variables in the model may change. Therefore, the study offers a novel perspective to the literature. In future studies, researchers could investigate how imbalances in the proportion of “1” versus “0” observations affect the detection model.

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Cite This Article

Çağlak, E., Çakır, H. M. (2025). Redefining Financial Manipulation Detection: A New Model's Performance Against the M-Score. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 12 Issue: 2. https://doi.org/10.30798/makuiibf.1606502

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References (51)

  1. Amara, I., Amar, A. B., & Jarboui, A. (2013). Detection of fraud in financial statements: French companies as a case study. International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(3), 0-51. https://doi.org/10.6007/IJARAFMS/v3-i3/34
  2. Aris, N. A., Othman, R., Arif, S. M. M., Malek, M. A. A., & Omar, N. (2013). Fraud detection: Benford’s law vs Beneish model. In IEEE Symposium on Humanities, Science and Engineering Research, (June),726-731.
  3. Beasley, M.S., (1996). An empirical analysis of the relation between board of director composition and financial statement fraud, Accounting Review, 71(4), 443–65.
  4. Beasley, M. S., Hermanson, D. R., Carcello, J. V., & Neal, T. L. (2010). Fraudulent financial reporting: 1998-2007: An analysis of US public companies. American Institute of Certified Public Accountants. https://egrove.olemiss.edu/cgi/viewcontent.cgi?article=1534&context=aicpa_assoc
  5. Benligiray, S., & Onay, A. (2021). Kazanç manipülasyonu tespit modellerinin Borsa İstanbul şirketlerinde test edilmesi. Muhasebe ve Denetime Bakış, 21(64), 179-204.
  6. Brennan, N., &. Hennessy J. (2001). Forensic accounting. Round Hall Sweet & Maxwell.
  7. DeAngelo, L. (1986). Accounting numbers as market valuation substitutes: A study of management buyouts of public stockholders. The Accounting Review, 6(1), 400-420.
  8. Guy, D. M., & Pany, K. (1997). Fraud in a financial statement audit Online unter. https://www.aicpa-cima.com/home.
  9. Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1-3), 85-107. https://doi.org/10.1016/0165-4101(85)90029-1
  10. Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29, 193-228. https://doi.org/10.2307/2491047
  11. Barton, J., & Simko, P. J. (2002). The balance sheet as an earnings management constraint. The accounting review, 77(1), 1-27. https://doi.org/10.2308/accr.2002.77.s-1.1
  12. Bekçi, İ., & Avğarlıgil, N. (2011). Finansal bilgi manipülasyonu yöntemlerinden yaratici muhasebe ve bir uygulama. World of Accounting Science, 13(2). 133-162.
  13. 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. https://doi.org/10.1016/S0278-4254(97)00023-9
  14. Beneish, M. D., (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296
  15. Çağlak, E., & Çakır, H. M. (2024). Türkiye’de finansal bilgi manipülasyonun tespit ve tahmini ile ilgili ampirik çalışma. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 130-157. https://doi.org/10.25287/ohuiibf.1325130
  16. Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
  17. Demetriades, P., & Owusu-Agyei, S. (2022). Fraudulent financial reporting: An application of fraud diamond to Toshiba’s accounting scandal. Journal of Financial Crime, 29(2), 729-763. https://doi.org/10.1108/JFC-05-2021-0108
  18. Erdoğan, M., & Erdoğan, E. O. (2020). Financial statement manipulation: A Beneish model application. In Contemporary Issues in Audit Management and Forensic Accounting (pp. 173-188). Emerald Publishing Limited. https://doi.org/10.1108/S1569-375920200000102014
  19. Fratrič, P., Sileno, G., Klous, S., & van Engers, T. (2022). Manipulation of the Bitcoin market: An agent-based study. Financial Innovation, 8(1), 60. https://doi.org/10.1186/s40854-022-00364-3
  20. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. McGraw-Hill/Irwin.
  21. Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139-152. https://dx.doi.org/10.1016/j.knosys.2017.05.001
  22. Halilbegovic, S., Celebic, N., Cero, E., Buljubasic, E., & Mekic, A. (2020). Application of Beneish M-score model on small and medium enterprises in Federation of Bosnia and Herzegovina. Eastern Journal of European Studies, 11(1), 146-163.
  23. Hasan, M. S., Omar, N., Barnes, P., & Handley-Schachler, M. (2017). A cross-country study on manipulations in financial statements of listed companies: Evidence from Asia. Journal of Financial Crime, 24(4), 656-677. https://doi.org/10.1108/JFC-07-2016-0047
  24. Herawati, N., & Tarjo (2017). The comparison of two data mining method to detect financial fraud in Indonesia. Accounting and Finance Review, 2(1), 1-8 https://doi.org/10.35609/afr.2017.2.1(1)
  25. Hosmer, D. W., & S. Lemeshow, (1989). Applied Logistic Regression. John Wiley & Sons.
  26. Hung, D. N., Ha, H. T. V., & Binh, D. T. (2017). Application of F-score in predicting fraud, errors: Experimental research in Vietnam. International Journal of Accounting and Financial Reporting. 7(2), 303-322. https://doi.org/10.5296/ijafr.v7i2.12174
  27. Kara, E., Ugurlu, M., & Korpi, 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. https://doi.org/10.56578/jafas010102
  28. Kanapickiene, R., & Grundiene, Ž. (2015). The model of fraud detection in financial statements by means of financial ratios. Procedia-Social and Behavioral Sciences, 213(2), 321–327. https://doi.org/10.1016/j.sbspro.2015.11.545
  29. Kedia, S., & Philippon, T. (2009). The economics of fraudulent accounting. The Review of Financial Studies, 22(6), 2169-2199. https://doi.org/10.1093/rfs/hhm016
  30. Küçükkocaoğlu, G., & Küçüksözen, C. (2005). Gerçeğe aykırı finansal tabloların ortaya çıkarılması: IMKB şirketleri üzerine amprik bir çalışma. Muhasebe ve Finansman Dergisi, 28, 160-171.
  31. Küçükkocaoğlu, G., Benli Keskin, B., & Küçüksözen, C. (2007). Finansal bilgi manipülasyonunun tespitinde yapay sinir ağı modelinin kullanımı. IMKB Dergisi, 9(36), 1-30.
  32. Lehenchuk, S., Mostenska, T., Tarasiuk, H., Polishchuk, I., & Gorodysky, M. (2020, November). Financial statement fraud detection of Ukrainian corporations on the basis of beneish model. In International Conference on Business and Technology (pp. 1341-1356). Springer International Publishing. https://doi.org/10.1007/978-3-030-69221-6_100
  33. Li, S., Wang, G., & Luo, Y. (2022). Tone of language, financial disclosure, and earnings management: A textual analysis of form 20-F. Financial Innovation, 8(1), 43. https://doi.org/10.1186/s40854-022-00346-5
  34. Lin, C. C., Chiu, A. A., Huang, S. Y., & Yen, D. C. (2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowledge-Based Systems, 89, 459–470. https://doi.org/10.1016/j.knosys.2015.08.011
  35. Lotfi, N., & Aghaei Chadegani, A. (2018). Detecting corporate financial fraud using Beneish M-Score Model. International Journal of Finance & Managerial Accounting, 2(8), 29-34.
  36. 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.
  37. Matsumura, E. M., & Tucker, R. R. (1992). Fraud detection: A theoretical foundation. Accounting Review, 67(4) 753-782.
  38. Mehta, A., & Bhavani, G. (2017). Application of forensic tools to detect fraud: The case of Toshiba. Journal of Forensic and Investigative Accounting, 9(1), 692-710
  39. Ujal, M., Amit, P., Hiral, P., & Rajen, P. (2012). Detection of fraudulent financial statement in India: An exploratory study. Global Science Research Journals, 4, 1-19.
  40. Ramírez-Orellana, A., Martínez-Romero, M. J. & Mariño-Garrido, T. (2017). Measuring fraud and earnings management by a case of study: Evidence from an international family business. European Journal of Family Business, 7(1-2), 41-53. https://doi.org/10.1016/j.ejfb.2017.10.001
  41. Spathis C. T. (2002). Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191. https://doi.org/10.1108/02686900210424321
  42. Song, Y., Wang, H., & Zhu, M. (2018). Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR. Financial Innovation, 4, 1-14. https://doi.org/10.1186/s40854-018-0086-0
  43. Svabova. L., Kramarova, K., Chutka, J., & Strakova, L. (2020). Detecting earnings manipulation and fraudulent financial reporting in Slovakia. Oeconomia Copernicana, 11(3), 485–508. https://doi.org/10.24136/oc.2020.020
  44. Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38–38. https://doi.org/10.19030/jabr.v11i3.5858
  45. 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/10.1016/j.adiac.2010.10.004
  46. Ravenda, D., Valencia-Silva, M. M., Argiles-Bosch, J. M., & Garcia-Blandon, J. (2018). Accrual management as an indication of money laundering through legally registered Mafia firms in Italy. Accounting, Auditing & Accountability Journal, 31(1), 286-317. https://doi.org/10.1108/AAAJ-12-2015-2329
  47. ROIC.ai. (n.d.). ROIC.ai. https://www.roic.ai/
  48. Securities and Exchange Commission (2009). Accounting and Auditing Enforcement. https://www.sec.gov/divisions/enforce/friactions?aId=edit-year&year=2009&month=All
  49. Situngkir, N. C., & Triyanto, D. N. (2020). Detecting fraudulent financial reporting using fraud score model and fraud pentagon theory: Empirical study of companies listed in the LQ 45 Index. The Indonesian Journal of Accounting Research, 23(3), 373-410.
  50. Tran, M. D., Dang, N. H., & Hoang, T. V. H. (2018) Research on misstatements in financial statements: The case of listed firms on Ho Chi Minh City stock exchange. International Journal of Applied Business and Economic Research, 15(23), 499-518.
  51. Zainuddin, E. F., & Hashim, H. A. (2016). Detecting fraudulent financial reporting using financial ratio. Journal of Financial Reporting and Accounting, 15, 266–278. https://doi.org/10.1108/JFRA-05-2015-0053