Application of the Beneish Model on the Warsaw Stock Exchange

• Authors: Marek Sylwestrzak

Abstracts

EN

This paper investigates irregularities in financial statements by applying the Beneish and Roxas models to Polish firms listed on the Warsaw Stock Exchange from 2015 to 2020. The total sample included 110 observations. The sample comprised companies that had received an adverse or disclaimer opinion by the auditors, but had not been fined by the Polish Financial Supervision Authority (KNF Board). The control firms were selected based on the industry as selected by the standard industrial classification code and on the financial year, with minimizing the difference in the size of total assets. The results indicate that the Roxas model revealed greater accuracy than the Beneish model on the tested sample. The use of logistic regression allowed a modification of the Beneish model to align it with the conditions of the Polish market. The modified Beneish model showed greater accuracy for the tested sample and companies fined by the KNF Board.

Keywords

EN

Beneish model; Roxas model; Warsaw Stock Exchange; logistic regression

• Publisher: Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
• Journal: Journal of Banking and Financial Economics
• Year: 2022
• Issue: 2(18)
• Pages: 5-16

Dates

published

2022

Contributors

author

Marek Sylwestrzak

• University of Warsaw, Faculty of Economic Sciences Poland

• https://orcid.org/0000000189628168

References

• Alfian, F., & Triani, N. (2019). Fraudulent financial reporting detection using Beneish M-Score model in public companies in 2012–2016. Asia Pacific Fraud Journal, 4(1), 27–42.
• 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), 40–51. https://doi.org/10.6007/IJARAFMS/v3-i3/34
• Anh, N., & Nguyen, H. (2016). Using the M-score model in detecting earnings management: Evidence from nonfinancial Vietnamese listed companies. VNU Journal of Science: Economics and Business, 32(2), 14–23.
• Anning, A., & Adusei, M. (2020). An analysis of financial statement manipulation among listed manufacturing and trading firms in Ghana. Journal of African Business, 1–15. https://doi.org/10.1080/15228916.2020.1826856
• Artienwicz, N., Bartoszewicz, A., Cygańska, M., & Wójtowicz, P. (2020). Kształtowanie wyniku finansowego w Polsce. Teoria-praktyka-stan badań. IUS PUBLICUM.
• Association of Certified Fraud Examiners. (2020) Report to the Nations 2020. Global Study on Occupational Fraud and Abuse. Retrieved from https://acfepublic.s3-us-west-2.amazonaws.com/2020-Report-to-the-Nations.pdf
• Ata, H., & Seyrek, I. (2009). The use of data mining techniques in detecting fraudulent financial statements: An application on manufacturing firms. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(2), 157–170.
• Beasley, M., Hermanson, D., Carcello, J., & Neal, T. (2010). Fraudulent financial reporting: 1998–2007: An analysis of US public companies. COSO Committee of Sponsoring Organizations of the Treadway Commission. Retrieved from https://www.coso.org/Documents/COSO-Fraud-Study-2010-001.pdf
• Beneish, M. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36. https://doi.org/10.2469/faj.v55.n5.2296
• Beneish, M., Lee, Ch., & Craig, D. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57–82. https://doi.org/10.2469/faj.v69.n2.1
• Buljubasic, E., & Halilbegovic, S. (2017, January). Detection of financial statement fraud using Beneish model [Paper presentation]. International Conference on Economic and Social Studies. https://doi.org/10.14706/icesos178
• Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5(1), 89–105. https://doi.org/10.1186/s40064-016-1707-6
• Comporek, M. (2020). The effectiveness of the Beneish model in the detection of accounting violations – The example of companies sanctioned by the Polish Financial Supervision Authority. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 64(10), 18–30. https://doi.org/10.15611/pn.2020.10.2
• Dechow, P., G., W., Larson, C., & Sloan, R. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
• Dong, W., Shaoyi, L., & Zhongju, Z. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461–487. https://doi.org/10.1080/07421222.2018.1451954
• Erdogan, M., & Erdogan, E. (2020). Financial Statement Manipulation: A Beneish Model Application. In S. Grima, E. Boztepe, & P. Baldacchino (Eds.), Contemporary Issues in Audit Management and Forensic Accounting (Vol. 102, pp. 173–188). Emerald Publishing Limited. https://doi.org/10.1108/S1569-375920200000102014
• Feruleva, N., & Shtefan, M. (2017). Detecting financial statements fraud: The evidence from Russia. Journal of Corporate Finance Research, 11(2), 32–45. https://doi.org/10.17323/j.jcfr.2073-0438.11.2.2017.32-45
• Gaganis, C. (2009). Classification techniques for the identification of falsified financial statements: A comparative analysis. Intelligent Systems in Accounting, Finance & Management: International Journal, 16(3), 207–229. https://doi.org/10.1002/isaf.303
• Golec, A. (2019). Ocena skuteczności modelu Beneisha w wykrywaniu manipulacji w sprawozdaniach finansowych. Acta Universitatis Lodziensis. Folia Oeconomica, 2(341), 161–182. https://doi.org/10.18778/0208-6018.341.10
• Gupta, R., & Gill, N. (2012). Prevention and Detection of Financial Statement Fraud – An Implementation of Data Mining Framework. International Journal of Advanced Computer Science and Applications, 3(8), 150–160. https://doi.org/10.14569/IJACSA.2012.030825
• 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://doi.org/10.1016/j.knosys.2017.05.001
• 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.
• Hasan, M., 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
• Herawati, N. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Procedia – Social and Behavioral Sciences, 211, 924–930. https://doi.org/10.1016/j.sbspro.2015.11.122
• Hołda, A. (2020). Using the Beneish M-Score model: Evidence from non-financial companies listed on the Warsaw Stock Exchange. Investment Management & Financial Innovations, 17(4), 389–401. https://doi.org/10.21511/imfi.17(4).2020.33
• Jan, C. (2018). An effective financial statements fraud detection model for the sustainable development of financial markets: Evidence from Taiwan. Sustainability, 10(2), 1–14. https://doi.org/10.3390/su10020513
• Kanapickiene, R., & Grundiene, Z. (2015). The model of fraud detection in financial statements by means of financial ratios. Procedia – Social and Behavioral Sciences, 213, 321–327. https://doi.org/10.1016/j.sbspro.2015.11.545
• Kramarova, K., & Valaskova, K. (2020). Application of chosen fraudulent detection technique in the Slovak business environment. SHS Web of Conferences, 74, 01019. https://doi.org/10.1051/shsconf/20207401019
• Lehenchuk, S., Mostenska, T., Tarasiuk, H., Polishchuk, I., & Gorodysky, M. (2021). Financial statement fraud detection of Ukrainian corporations on the basis of Beneish model. In B. Alareeni, A. Hamdan, & I. Elgedawy (Eds.), The importance of new technologies and entrepreneurship in business development: In the context of economic diversity in developing countries (pp. 1341–1356). Springer. https://doi.org/10.1007/978-3-030-69221-6_100
• Lev, B., & Thiagarajan, R. (1993). Fundamental information analysis. Journal of Accounting Research, 31(2), 190–215. https://doi.org/10.2307/2491270
• Mantone, P. (2013). Using analytics to detect possible fraud: Tools and techniques. John Wiley & Sons. https://doi.org/10.1002/9781118715789
• Ozcan, A. (2016). Firm characteristics and accounting fraud: A multivariate approach. Journal of Accounting, Finance and Auditing Studies, 2(2), 128–144.
• Ozcan, A. (2018). The use of Beneish model in forensic accounting: Evidence from Turkey. Journal of Applied Economics & Business Research, 8(1), 57–67.
• Paolone, F., & Magazzino, C. (2014). Earnings manipulation among the main industrial sectors. Evidence from Italy. Economia Aziendale Online, 5(4), 253–261. http://dx.doi.org/10.4485/ea203-5498.005.0022
• Papik, M., & Papikova, L. (2020). Detection models for unintentional financial restatements. Journal of Business Economics and Management, 21(1), 64–86. https://doi.org/10.3846/jbem.2019.10179
• Piasecki, M. (2015). Kształtowanie wyników finansowych a wielkość zaciąganego kapitału obcego spółek notowanych na Giełdzie Papierów Wartościowych w Warszawie. Finanse, Rynki Finansowe, Ubezpieczenia, 73, 219–228.
• Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063–1073. https://doi.org/10.1108/JFC-11-2014-0055
• Rezaee, Z. (2005). Causes, consequences, and deterence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277–298. https://doi.org/10.1016/S1045-2354(03)00072-8
• Roxas, M. (2011). Financial statement fraud detection using ratio and digital analysis. Journal of Leadership, Accountability, and Ethics, 8(4), 56–66.
• Sabău (Popa), A.-I., Mare, C., & Safta, I. L. (2021). A statistical model of fraud risk in financial statements. Case for Romania companies. Risks, 9(116), 1–15. https://doi.org/10.3390/risks9060116
• Shakouri, M., Taherabadi, A., Ghanbari, M., & Jamshidinavid, B. (2021). Explaining the Beneish model and providing a comprehensive model of fraudulent financial reporting (FFR). International Journal of Nonlinear Analysis and Applications, 12, 39–48.
• Sorkun, M., & Toraman, T. (2017). Fraud detection on financial statements using data mining techniques. Intelligent Systems and Applications in Engineering, 5(3), 132–134. https://doi.org/10.18201/ijisae.2017531428
• Spathis, C., Doumpos, M., & Zopounidis, C. (2002). Detecting falsified financial statements: A comparative study using multicriteria analysis and multivariate statistical techniques. European Accounting Review, 11(3), 509–535. https://doi.org/10.1080/0963818022000000966
• Summers, S., & Sweeney, J. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73(1), 131–146.
• 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
• Vetoshkina, E., Cherepanova, P., Semenikhina, N., & Tukhvatullin, R. (2020). Financial statements fraud detection: Analysis of Beneish and Roxas models applicability (Russian approach). Helix, 10(5), 212–217. https://doi.org/10.29042/2020-10-5-212-217
• Wyrobek, J., Poplawski, L., & Surówka, M. (2020). Identification of a fraudulent organizational culture in enterprises listed in Warsaw Stock Exchange. European Research Studies Journal, 23(2), 622–637. https://doi.org/10.35808/ersj/1845
• Yao, J., Pan, Y., Yang, S., Chen, Y., & Li, Y. (2019). Detecting fraudulent financial statements for the sustainable development of the socio-economy in China: A multi-analytic approach. Sustainability, 11(6), 1–17. https://doi.org/10.3390/su11061579

Identifiers

DOI

10.7172/2353-6845.jbfe.2022.2.1

Biblioteka Nauki

2163460