Predicting financial statement manipulation in South Africa: A comparison of the Beneish and Dechow models

References

• Ackerman, S., Raz, O., Zalmanovici, M., & Zlotnick, A. 2019. Automatically detecting data drift in machine learning classifiers. Engineering Dependable and Secure Machine Learning Systems workshop at AAAI 2019 conference. Honolulu, Hawaii.
   Google Scholar
• Aghghaleh, S. F., Mohamed, Z. M., & Rahmat, M. M. (2016). Detecting financial statement frauds in Malaysia: Comparing the abilities of Beneish and Dechow models. Asian Journal of Accounting and Governance, 7(2016), 57–33. https://doi.org/10.17576/AJAG-2016-07-05
   Google Scholar
• Albizri, A., Appelbaum, D., & Nicholas, R. (2019). Evaluation of financial statements fraud detection research: A multi-disciplinary analysis. International Journal of Disclosure and Governance, 16(4), 206–241. https://doi.org/10.1057/s41310-019-00067-9
   Web of Science ®Google Scholar
• Association of Certified Fraud Examiners. 2020. Report to the nations: 2020 global study on occupational fraud and abuse . Available [Accessed 22 November. 2021]: https://acfepublic.s3-us-west-2.amazonaws.com/2020-Report-to-the-Nations.pdf
   Google Scholar
• Association of Certified Fraud Examiners. 2021. The next normal: Preparing for a post-pandemic fraud landscape [Online]. Available [Accessed 14 January. 2022]: https://www.acfe.com/uploadedFiles/ACFE_Website/Content/covid19/Covid-19-Preparing-for-a-Post-Pandemic-Fraud-Landscape.pdf
   Google Scholar
• Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Beneish, M. D., Lee, C. M. C., & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57–82. https://doi.org/10.2469/faj.v69.n2.1
   Web of Science ®Google Scholar
• Beneish, M. D., & Vorst, P. 2021. The cost of fraud prediction errors. The Accounting Review. Advance online publication: [Accessed 19 July 2022] https://doi.org/10.2308/TAR-2020-0068
   Google Scholar
• Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
   Web of Science ®Google Scholar
• Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146–1160. https://doi.org/10.1287/mnsc.1100.1174
   Web of Science ®Google Scholar
• Chakrabarty, B., Moulton, P. C., Pugachev, L., & Wang, X. 2022. Catch me if you can: In search of accuracy, scope, and ease of fraud prediction. SSRN working paper. Available[Accessed 19 July 2022]: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3352667
   Google Scholar
• Comporek, M. (2020). The effectiveness of the Beneish model in the detection of accounting violations – the example of companies sanctioned by the Polish Financial Supervisory Authority. Prace Naukowe Uniwersytetu Ekonomicznego We Wroclawiu, 64(10), 18–30. https://doi.org/10.15611/pn.2020.10.02
   Google Scholar
• Dechow, P. M., Ge, W., Larson, C., & 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
   Web of Science ®Google Scholar
• Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193–225.
   Web of Science ®Google Scholar
• Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13(1), 1–36. https://doi.org/10.1111/j.1911-3846.1996.tb00489.x
   Google Scholar
• Deniswara, K., Kesuma, J. T., & Louis, V. 2022. Forensic accounting on Indonesian energy sector with Beneish M-score model analysis. ICEEEG’22: Proceedings of the 6thInternational Conference on E-Commerce, E-Business and E-Government. Plymouth, United Kingdom.
   Google Scholar
• Enomoto, M., Kimura, F., & Yamaguchi, T. (2015). Accrual-based and real earnings management: An international comparison for investor protection. Journal of Contemporary Accounting & Economics, 11(3), 183–198. https://doi.org/10.1016/j.jcae.2015.07.001
   Google Scholar
• Herawati, N. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Procedia - Social and Behavioral Sciences, 211(2015), 924–930. https://doi.org/10.1016/j.sbspro.2015.11.122
   Google Scholar
• Hlobo, M., Moloi, T., & Marx, B. (2022). Framework for screening and evaluating the competencies and qualities of the board of directors in South Africa’s stated-owned companies. Journal of Risk and Financial Management, 15(11), 1–15. https://doi.org/10.3390/jrfm15110492
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Jones, K. L., Krishnan, G. V., & Melendrez, K. D. (2008). Do models of discretionary accruals detect actual cases of fraudulent and restated earnings? An empirical analysis. Contemporary Accounting Research, 25(2), 499–531. https://doi.org/10.1506/car.25.2.8
   Web of Science ®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. Jurnal Pengurusan, 46(2016), 23–32. https://doi.org/10.17576/pengurusan-2016-46-03
   Google Scholar
• Karpoff, J. M., Koester, A., Lee, D. S., & Martin, G. S. (2017). Proxies and databases in financial misconduct research. The Accounting Review, 92(6), 129–163. https://doi.org/10.2308/accr-51766
   Web of Science ®Google Scholar
• Koornhof, C., & Du Plessis, D. (2000). Red flagging as an indicator of financial statement fraud: The perspective of investors and lenders. Meditari Accountancy Research, 8(1), 69–93. https://doi.org/10.1108/10222529200000005
   Google Scholar
• Kukreja, G., Gupta, S. M., Sarea, A. M., & Kumaraswamy, S. (2020). Beneish M-score and Altman Z-score as a catalyst for corporate fraud detection. Journal of Investment Compliance, 21(4), 231–241. https://doi.org/10.1108/JOIC-09-2020-0022
   Google Scholar
• Larcker, D. F., & Zakolyukina, A. A. (2012). Detecting deceptive discussions in conference calls. Journal of Accounting Research, 50(2), 495–540. https://doi.org/10.1111/j.1475-679X.2012.00450.x
   Web of Science ®Google Scholar
• Lo, K., Ramos, F., & Rogo, R. (2017). Earnings management and annual report readability. Journal of Accounting and Economics, 63(1), 1–25. https://doi.org/10.1016/j.jacceco.2016.09.002
   Web of Science ®Google Scholar
• Lu, W., & Zhao, X. (2021). Research and improvement of fraud identification model of Chinese A-share listed companies based on M-score. Journal of Financial Crime, 28(2), 566–579. https://doi.org/10.1108/JFC-12-2019-0164
   Google Scholar
• Marinakis, P. 2011. An investigation of earnings management and earnings manipulation in the UK. Unpublished PhD thesis, University of Nottingham.
   Google Scholar
• Mavengere, K. (2015). Predicting corporate bankruptcy and earnings manipulation using the Altman Z-score and Beneish M-score. The case of manufacturing firm in Zimbabwe. International Journal of Management Sciences and Business Research, 4(10), 8–14.
   Google Scholar
• Mishra, M., & Malhotra, A. K. (2016). Earnings management practices in Indian companies: A cross-sectional analysis. Journal of Modern Accounting and Auditing, 12(6), 295–305. https://doi.org/10.17265/1548-6583/2016.06.001
   Google Scholar
• Moepya, S. O. 2017. Enhancing the detection of financial statement fraud through the use of missing value estimation, multivariate filter feature selection and cost-sensitive classification. Unpublished PhD thesis, University of Johannesburg.
   Google Scholar
• Moepya, S. O., Akhoury, S. S., & Nelwamondo, F. V. 2014. Applying cost-sensitive classification for financial statement fraud detection under high class-imbalance. 2014 IEEE International Conference on Data Mining Workshop. Shenzhen, China. 183–192.
   Google Scholar
• Moepya, S. O., Akhoury, S. S., Nelwamondo, F. V., & Twala, B. (2016). The role of imputation in detecting fraudulent financial reporting. International Journal of Innovative Computing, Information and Control, 12(1), 333–356.
   Google Scholar
• Moepya, S. O., Nelwamondo, F. V., & Van Der Walt, C. (2014). A support vector machine approach to detect financial statement fraud in South Africa: A first look. In N. T. Nguyen, B. Attachoo, B. Trawinski, & K. Somboonviwat (Eds.), Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science (pp. 42–51). Springer.
   Google Scholar
• Mongwe, W. T., & Malan, K. M. (2020). A survey of automated financial statement fraud detection with relevance to the South African context. South African Computer Journal, 32(1), 74–112. https://doi.org/10.18489/sacj.v32i1.777
   Google Scholar
• Muzata, T., & Marozva, G. (2022). Executive compensation schemes: Accelerants of agency and corporate governance problems in South Africa. African Journal of Governance and Development, 11(1.2), 328–350. https://doi.org/10.36369/2616-9045/2022/v11si2a7
   Google Scholar
• Nyakarimi, S. (2022). Probable earning manipulation and fraud in banking sector. Empirical study from East Africa. Cogent Economics & Finance, 10(1), 1–20. https://doi.org/10.1080/23322039.2022.2083477
   Web of Science ®Google Scholar
• Nyakarimi, S. N., Kariuki, S. N., & Kariuki, P. W. (2020). Financial statement manipulations using Beneish model and probit regression model: A case of banking sector in Kenya. European Online Journal of Natural and Social Sciences, 9(1), 253–264.
   Google Scholar
• Okiro, K., & Otiso, D. O. (2021). Detection of fraud in financial statements using Beneish ratios for companies listed at Nairobi Securities Exchange. African Development Finance Journal, 5(1), 92–126.
   Google Scholar
• Onyebuchi, O. H., & Nkem, A. T. (2021). Forensic accounting and economic crime: A study of selected conglomerate firms in Nigeria. Advance Journal of Management, Accounting and Finance, 6(7), 37–67.
   Google Scholar
• Orazalin, N., & Akhmetzhanov, R. (2018). Earnings management, audit quality, and cost of debt: Evidence from a Central Asian economy. Managerial Auditing Journal, 34(6), 696–721. https://doi.org/10.1108/MAJ-12-2017-1730
   Web of Science ®Google Scholar
• Paolone, F., & Magazzino, C. (2015). Earnings manipulation among the main industrial sectors. Evidence from Italy. Economia Aziendale Online, 5(4), 253–261.
   Google Scholar
• Perols, J. L., & Lougee, B. A. (2011). The relationship between earnings management and financial statement fraud. Advances in Accounting, Incorporating Advances in International Accounting, 27(1), 39–53. https://doi.org/10.1016/j.adiac.2010.10.004
   Google Scholar
• Price, R. A., Sharp, N. Y., & Wood, D. A. (2011). Detecting and predicting accounting irregularities: A comparison of commercial and academic risk measures. Accounting Horizons, 25(4), 755–780. https://doi.org/10.2308/acch-50064
   Web of Science ®Google Scholar
• PricewaterhouseCoopers. 2020. Economic crime - When the boardroom becomes the battlefield. Available[Accessed 17 January. 2022]: https://www.pwc.co.za/en/assets/pdf/global-economic-crime-survey-2020.pdf
   Google Scholar
• Pududu, M. L., & De Villiers, C. (2016). Earnings management through loss avoidance: Does South Africa have a good story to tell? South African Journal of Economic and Management Sciences, 19(1), 18–34. https://doi.org/10.4102/sajems.v19i1.1124
   Web of Science ®Google Scholar
• Putra, A. N., & Dinarjito, A. (2021). The effect of fraud pentagon and F-score model in detecting fraudulent financial reporting in Indonesia. Jurnal Ilmiah Akuntansi dan Bisnis, 16(2), 247–263. https://doi.org/10.24843/JIAB.2021.v16.i02.p05
   Google Scholar
• Rabin, C. E. 2016. Earnings management in South Africa: Evidence and implications. Unpublished PhD thesis, University of the Witwatersrand.
   Google Scholar
• Rad, M., Amiri, A., Ranjbar, M. H., & Salari, H. (2021). Predictability of financial statements fraud-risk using Benford’s Law. Cogent Economics & Finance, 9(1), 1–27. https://doi.org/10.1080/23322039.2021.1889756
   Web of Science ®Google Scholar
• Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, İ. (2005). Accrual reliability, earnings persistence and stock prices. Journal of Accounting and Economics, 39(3), 437–485. https://doi.org/10.1016/j.jacceco.2005.04.005
   Web of Science ®Google Scholar
• Rossouw, J., & Styan, J. (2019). Steinhoff collapse: A failure of corporate governance. International Review of Applied Economics, 33(1), 163–170. https://doi.org/10.1080/02692171.2019.1524043
   Web of Science ®Google Scholar
• Skousen, C. J., & Twedt, B. J. (2009). Fraud score analysis in emerging markets. Cross Cultural Management: An International Journal, 16(3), 301–316. https://doi.org/10.1108/13527600910977373
   Google Scholar
• Van Der Linde, K. E. (2022). The Steinhoff corporate scandal and the protection of investors who purchased shares on the secondary market. Potchefstroom Electronic Law Journal, 25(1), 1–23. https://doi.org/10.17159/1727-3781/2022/v25i0a14876
   Google Scholar
• Walker, S. 2020. A needle found: Machine learning does not significantly improve corporate fraud detection beyond a simple screen on sales growth. SSRN working paper. Available[Accessed 20 November 2021]: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3739480
   Google Scholar
• Watson, S., & Rossouw, J. (2012). JSE efficiency and share price reaction to forced financial restatements. Journal of Economic and Financial Sciences, 5(2), 417–436. https://doi.org/10.4102/jef.v5i2.292
   Google Scholar
• Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., & Petitjean, F. (2016). Characterizing concept drift. Data Mining and Knowledge Discovery, 30(4), 964–994. https://doi.org/10.1007/s10618-015-0448-4
   Web of Science ®Google Scholar
• Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
   Google Scholar
• World Bank. 2020. Gross domestic product 2020. Available: https://databank.worldbank.org/data/download/GDP.pdf [Accessed 17 January. 2022].
   Google Scholar
• World Economic Forum. 2017. The global competitiveness report 2016-2017. Available[Accessed 3 August. 2017]: http://www3.weforum.org/docs/GCR2016-2017/05FullReport/TheGlobalCompetitivenessReport2016-2017_FINAL.pdf
   Google Scholar