Ocena skuteczności modelu Beneisha w wykrywaniu manipulacji w sprawozdaniach finansowych

Anna Golec

doi:10.18778/0208-6018.341.10

Authors

Anna Golec University of Gdansk, Faculty of Management, Department of Corporate Finance https://orcid.org/0000-0001-7174-6246

DOI:

https://doi.org/10.18778/0208-6018.341.10

Keywords:

Beneish Model, M-Score, financial statement manipulation, Poland, listed companies

Abstract

The aim of this study is to verify whether Beneish M‑Score model can be useful in detecting Polish companies involved in earning management practices that lead to adverse or disclaimer of auditors’ opinion. The sample covers 24 pairs of firms listed on Warsaw Stock Exchange or New Connect (alternative market). The findings generally indicate that with –2.22 point cut‑off the model was able to identify 67% of manipulators and 75% non‑manipulators correctly. The accuracy of the model improved from 71% to 75% after shifting the cut‑off point to –1.98. Another observation was that high changes in M‑Score values turned out to be better indicator of manipulation and the classification based on 35% change in year‑to‑year values reached 85% accuracy.

Downloads

Download data is not yet available.

References

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.

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.

Beneish M. D. (1999), The detection of earnings manipulation, „Financial Analysts Journal”, t. 55, nr 5, s. 24–36.

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.

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.

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.

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.

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.

Dechow P. M., Sloan R. G., Sweeney A. P. (1995), Detecting earnings management, „The Accounting Review”, nr 70, s. 193–193.

El Diri M. (2018), Introduction to earning management, Springer International Publishing, Cham.

Fich E. M., Shivdasani A. (2007), Financial Fraud, Director Reputation, and Shareholder Wealth, „Journal of Financial Economics”, nr 86(2), s. 306–333.

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.

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.

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.

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.

Jones J. (1991), Earnings management during import relief investigations, „Journal of Accounting Research”, nr 29(2), s. 193–228.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Marinakis P (2011), An investigation of earnings management and earnings manipulation in the UK, praca doktorska, Nottingham University.

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.

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.

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.

Paolone F., Magazzino C. (2014), Earnings manipulation among the main industrial sectors: Evidence from Italy, „Economia Aziendale”, nr 5, s. 253–261.

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.

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].

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.

Schilit H., Perler J. (2010), Financial Shenanigans: How to Detect Accounting Gimmicks & Fraud in Financial Reports, 3rd edition, McGraw‑Hill, New York.

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.

Spathis C. (2002), Detecting False Financial Statements Using Published Data: Some Evidence From Greece, „Managerial Auditing Journal”, nr 17(4), s. 179–191.

Stubben S. R. (2010), Discretionary revenues as a measure of earnings management, „The Accounting Review”, t. 85, nr 2, s. 695–717.

Summers S., Sweeney J. (1998), Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis, „Accounting Review”, nr 73(1), s. 131–146.

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].

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.

Ye J., (2007), Accounting Accruals and Tests of Earnings Management, https://ssrn.com/abstract=1003101 [dostęp: 1.02.2018].

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].