Ocena skuteczności modelu Beneisha w wykrywaniu manipulacji w sprawozdaniach finansowych
DOI:
https://doi.org/10.18778/0208-6018.341.10Słowa kluczowe:
model Beneisha, M‑Score, manipulacje wynikami finansowymi, Polska, rynek kapitałowyAbstrakt
Celem artykułu jest ocena, czy model Beneisha może stanowić użyteczne narzędzie do wykrywania manipulacji wynikami finansowymi, które prowadziły do wydania negatywnej opinii biegłego rewidenta lub odmowy jej wydania w polskich spółkach kapitałowych. Badaniem objęto 24 pary przedsiębiorstw z głównego rynku Giełdy Papierów Wartościowych w Warszawie oraz z rynku alternatywnego New Connect. Z przeprowadzonych analiz wynika, że przy punkcie granicznym –2,22 model poprawnie identyfikował 67% manipulatorów i 75% niemanipulatorów. Dokładność modelu wzrastała z 71% do 75% wraz z przesuwaniem punktu odcięcia do –1,98. Kolejną obserwacją był fakt, że duże zmiany w wartościach M‑Score okazały się lepszym kryterium oceny. Klasyfikacja podmiotów na podstawie 35% zmiany wskaźnika rok do roku pozwoliła zwiększyć dokładność grupowania do 85%.
Pobrania
Bibliografia
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”, t. 32, nr 2, s. 14–23.
Google Scholar
Ata H., Seyrek I. (2009), The Use of Data Mining Techniques in Detecting Fraudulent Financial Statements: An Application on Manufacturing Firms, „The Journal of Faculty of Economics and Administrative Sciences”, nr 14(2), s. 157–170.
Google Scholar
Beneish M. D. (1999), The detection of earnings manipulation, „Financial Analysts Journal”, t. 55, nr 5, s. 24–36.
Google Scholar
Beneish M. D., Lee C. M.C., Nichols D. C. (2013), Earnings Manipulation and Expected Returns, „Financial Analysts Journal”, t. 69, nr 2, s. 57–82.
Google Scholar
DeAngelo L. (1986), Accounting numbers as market valuation substitutes: A study of management buyouts of public stockholders, „The Accounting Review”, nr 61, s. 400–420.
Google Scholar
Dechow P. M., Dichev I. D. (2002), The quality of accruals and earnings: The role of accrual estimation errors, „The Accounting Review”, nr 77, s. 35–59.
Google Scholar
Dechow P. M., Richardson S. A., Tuna I. (2003), Why are earnings kinky? An examination of the earnings management explanation, „Review of Accounting Studies”, nr 8, s. 355–384.
Google Scholar
Dechow P. M., Sloan R. G. (1991), Executive incentives and the horizon problem: An empirical investigation, „Journal of Accounting and Economics”, nr 14, s. 51–89.
Google Scholar
Dechow P. M., Sloan R. G., Sweeney A. P. (1995), Detecting earnings management, „The Accounting Review”, nr 70, s. 193–193.
Google Scholar
El Diri M. (2018), Introduction to earning management, Springer International Publishing, Cham.
Google Scholar
Fich E. M., Shivdasani A. (2007), Financial Fraud, Director Reputation, and Shareholder Wealth, „Journal of Financial Economics”, nr 86(2), s. 306–333.
Google Scholar
Glancy F. H., Yadav S. B. (2011), A computational model for financial reporting fraud detection, „Decision Support Systems”, t. 50, cz. 3, s. 595–601.
Google Scholar
Gupta R., Gill N. (2012), Prevention and Detection of Financial Statement Fraud – An Implementation of Data Mining Framework, „Editorial Preface”, nr 3(8), s. 150–160.
Google Scholar
Hashim H. A., Salleh Z., Ariff A. M. (2013), The Underlying Motives for Earnings Management: Directors, Perspective, „International Journal of Trade, Economics and Finance”, t. 4, nr 5, s. 296–299.
Google Scholar
Johnson S., Ryan H., Tian Y. (2009), Managerial Incentives and Corporate Fraud: The Sources of Incentives Matter, „Review of Finance”, nr 13(1), s. 115–145.
Google Scholar
Jones J. (1991), Earnings management during import relief investigations, „Journal of Accounting Research”, nr 29(2), s. 193–228.
Google Scholar
Kamal M. E.M., Salleh M. F.M., Ahmad A. (2016), Detecting financial statement fraud by Malaysian public listed companies: The reliability of the Beneish M‑Score model, „Journal Pengurusan”, nr 46, s. 23–32.
Google Scholar
Kaminski K. A., Wetzel T. S., Guan L. (2004), Can financial ratios detect fraudulent financial reporting?, „Managerial Auditing Journal”, t. 19, cz. 1, s. 15–28.
Google Scholar
Kanapickienė R., Grundienė Ž. (2015), The Model of Fraud Detection in Financial Statements by Means of Financial Ratios, „Procedia – Social and Behavioral Sciences”, nr 213, s. 321–327.
Google Scholar
Kang S. H., Sivaramakrishnan K. (1995), Issues in testing earnings management and an instrumental variable approach, „Journal of Accounting Research”, nr 33, s. 353–367.
Google Scholar
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”, nr 1(1), s. 21–39.
Google Scholar
Kaur R., Sharma K., Khanna A. (2014), Detecting Earnings Management in India – A sector‑wise Study on European, „Journal of Business and Management”, t. 6, nr 11, s. 11–18.
Google Scholar
Kothari S. P., Leone A. J., Wasley C. E. (2005), Performance matched discretionary accrual measures, „Journal of Accounting and Economics”, nr 39, s. 163–197.
Google Scholar
Kotsiantis S., Koumanakos E., Tzelepis D., Tampakas V. (2006), Forecasting Fraudulent Financial Statements Using Data Mining, „International Journal of Computational Intelligence”, nr 3(2), s. 104–110.
Google Scholar
Mahama M. (2015), Detecting corporate fraud and financial distress using the Altman and Beneish models, „International Journal of Economics, Commerce and Management”, nr 3(1), s. 1–18.
Google Scholar
Marinakis P (2011), An investigation of earnings management and earnings manipulation in the UK, praca doktorska, Nottingham University.
Google Scholar
McNichols M. F. (2002), Discussion of: The quality of accruals and earnings – The role of accrual estimation errors, „The Accounting Review”, t. 77, nr s–1, s. 61–69.
Google Scholar
Omar N., Koya R. K., Sanusi Z. M., Shafie N.A (2014), Financial statement fraud: A Case examination using beneish model and ratio analysis, „International Journal of Trade, Economics and Finance”, t. 5, nr 2, s. 184–186.
Google Scholar
Pai P., Hsu M., Wang M. (2011), A Support Vector Machine‑Based Model for Detecting Top Management Fraud, „Knowledge‑Based Systems”, nr 24(2), s. 314–321.
Google Scholar
Paolone F., Magazzino C. (2014), Earnings manipulation among the main industrial sectors: Evidence from Italy, „Economia Aziendale”, nr 5, s. 253–261.
Google Scholar
Persons O. (1995), Using Financial Statement Data to Identify Factors Associated with Fraudulent Financial Reporting, „Journal of Applied Business Research”, nr 11(3), s. 38–46.
Google Scholar
Petrík V. (2016), Application of Beneish M‑Score on Selected Financial Statements, Conference: Bezpečné Slovensko a Európska Únia at: Košice, Slovakia – The University of Security Management in Košice, t. 1, https://www.researchgate.net/publication/311733912 [dostęp: 2.02.2018].
Google Scholar
Repousis S. (2016), Using Beneish model to detect corporate financial statement fraud in Greece, „Journal of Financial Crime”, t. 23 cz. 4, s. 1063–1073, https://doi.org/10.1108/JFC–11–2014–0055.
Google Scholar
Schilit H., Perler J. (2010), Financial Shenanigans: How to Detect Accounting Gimmicks & Fraud in Financial Reports, 3rd edition, McGraw‑Hill, New York.
Google Scholar
Skousen Ch.J., Twedt B. J. (2009), Fraud score analysis in emerging markets, „Cross Cultural Management: An International Journal”, t. 16, cz. 3, s. 301–316.
Google Scholar
Spathis C. (2002), Detecting False Financial Statements Using Published Data: Some Evidence From Greece, „Managerial Auditing Journal”, nr 17(4), s. 179–191.
Google Scholar
Stubben S. R. (2010), Discretionary revenues as a measure of earnings management, „The Accounting Review”, t. 85, nr 2, s. 695–717.
Google Scholar
Summers S., Sweeney J. (1998), Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis, „Accounting Review”, nr 73(1), s. 131–146.
Google Scholar
Sylwestrzak M. (2017), Wykorzystanie modelu CART‑Logit do analizy fałszerstw sprawozdań finansowych, „Finanse, Rynki Finansowe, Ubezpieczenia”, nr 4 (88/1), s. 403–412, http://dx.doi.org/10.18276/frfu.2017.88/1–39 [dostęp: 1.02.2018].
Google Scholar
Tarjo, Herawati N. (2015), Application of Beneish M‑Score Models and Data Mining to Detect Financial Fraud, „Procedia – Social and Behavioral Sciences”, nr 211, s. 924–930.
Google Scholar
Ye J., (2007), Accounting Accruals and Tests of Earnings Management, https://ssrn.com/abstract=1003101 [dostęp: 1.02.2018].
Google Scholar
Zaki M., Theodoulidis B. (2013), Analyzing Financial Fraud Cases Using a Linguistics‑Based Text Mining Approach, https://ssrn.com/abstract=2353834 or http://dx.doi.org/10.2139/ssrn.2353834 [dostęp: 1.02.2018].
Google Scholar
Pobrania
Opublikowane
Jak cytować
Numer
Dział
