Application Of Beneish M-Score Model In Detecting Probable Earnings Manipulation In Malaysian Public Listed Companies

NOR AQILAH(1), Nor Farizal Mohammed(2), Amrizah Kamaluddin(3*),

(1) Accounting Research Institute, Universiti Teknologi MARA, Malaysia
(2) Accounting Research Institute, Universiti Teknologi MARA, Malaysia
(3) Universiti Teknologi MARA Selangor, Malaysia
(*) Corresponding Author


Earnings manipulation involves alteration, adjustment, exploitation, and even timing of transactions in financial statements with the intention to report a sound economic performance in a company. This act could jeopardize the company’s financial performance in the long run and also make its stakeholders to suffer significant losses. Therefore, a tool to detect or predict earnings manipulation would be helpful to the stakeholders, practitioners, regulators, academicians, and professionals in the accounting field. Besides, an early detection tool is needed to alarm the enforcement agencies to make further investigations or legal actions. Therefore, the aim of this study is to analyse the Beneish M-Score models and its eight accounting variables to detect the likelihood to engage in earnings manipulation in the case of Malaysian PLCs (Public Listed Companies). The financial data of 80 of PLCs from 2015 to 2017 were gathered. This study applied Beneish Model as a detection tool for earnings manipulation and anomalies of red flags and to classify the companies into two groups, which are manipulators and non-likely manipulators. The Independent T-tests were analysed to identify dominating ratios. The results of this study found that M-Score and its three indexes were significantly different for manipulators and non-likely manipulators, which are Sales Growth Index (SGI), Total Accruals to Total Assets (TATA) and Days' Sales Receivable index (DSRI). The percentage of manipulators had slightly decreased in 2016 and gradually increased in 2017. Hence, the inflation or overestimation of sales and revenues, as well as accruals, could signals earnings manipulation.


Earnings manipulation, Beneish M-Score, eight accounting variables

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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, 57–65.

Ahmed, T., & Naima, J. (2016). Detection and analysis of probable earnings manipulation by firms in a developing country. Asian Journal of Business and Accounting, 9(1), 59–82.

Aris, N. A., Othman, R., Maznah, S., Arif, M., Affendi, M., & Malek, A. (2013). Fraud Detection : Benford ’ s Law vs Beneish Model. IEEE Symposium on Humanities, Science and Engineering Research, 726–731.

Arshad, R., Mohamed Iqbal, S., & Omar, N. (2015). Prediction of business failure and fraudulent financial reporting : Evidence from Malaysia. Indian Journal of Corporate Governance, 8(1).

Bartov, E. (1993). The timing earnings of asset sales and manipulation. Accounting Review, 68(4), 840–855.

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.

Beneish, M. D. (1999). The Detection of Earnings Manipulation. Financial Analysts Journal, 55(5), 24–36.

Bhavani, G., & Tabi, C. (2017). M-Score and Z-Score for detection of accounting fraud. Accountancy Business and the Public Interest, 68–86.

Bisogno, M., & De Luca, R. (2015). Financial distress and earnings manipulation: Evidence from Italian SMEs. Journal of Accounting and Finance, 4(1), 42–51. Retrieved from

Boon, H. T., Tze, S. O., & Lau, Y. (2017). Earnings management in Malaysian public listed family firms. Jurnal Pengurusan, 51(2017), 183–193. Retrieved from

Bursa Malaysia Securities Berhad. (2018). Questions and Answers in Relation to Bursa Malaysia Securities Berhad Listing Requirements for the Main Market. Retrieved May 17, 2019, from

Chariri, A., & Basundra, A. T. (2017). Does IFRS convergence decrease earning manipulation ? An empirical study of Indonesia. Advanced Science Letters, 23(8), 7066–7069.

Dalnial, H., Kamaluddin, A., Sanusi, Z. M., & Khairuddin, K. S. (2014). Detecting fraudulent financial reporting through financial statement analysis. Journal of Advanced Management Science, 2(1), 17–22.

Dechow, P. M., Sloan, R. G., Sweeney, A. P., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. Accounting Review, 70(2), 193–225.

Dikmen, B., & Güray, K. (2010). The detection of earnings manipulation : The three-phase cutting plane algorithm using mathematical programming. Journal of Forecasting, 29(5), 442–446.

Dimitrijevic, D. (2015). The detection and prevention of manipulations in the Balance sheet and the cash flow statement. Economic Horizons, 17(2), 135–150.

Dimitrijevic, D., Obradović, V., & Milutinović, S. (2018). Indicators of fraud in financial reporting in the Republic of Serbia. TEME, 12(4), 1319–1338.

Duffield, G., & Grabosky, P. (2001). The Psychology of Fraud. In Trends and Issues in Crime and Criminal Justice (Vol. 199). Retrieved from

Elsayed, A. A. (2017). Predictability of financial statements fraud-risk. (Doctoral dissertation, Northcentral University). Retrieved from ProQuest Dissertations and Theses database. (10271451).

Fawzi, N. S., Kamaluddin, A., & Sanusi, Z. M. (2015). Monitoring distressed companies through cash flow analysis. Procedia Economics and Finance, 28(April), 136–144.

Hair, J. F., Black, W. C., Bahin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). New Jersey: Pearson Prentice Hall.

Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7, 85–107.

Hogan, C. E., Rezaee, Z., Riley, R. A., & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. AUDITING: A Journal of Practice & Theory, 27(2), 231–252.

Jayakumar, K. (2018). Role of Forensic Accounting – An Analysis. IJRAR, 5(4), 221–232.

Johansson, E., & Carey, P. (2016). Detecting fraud: The role of the anonymous reporting channel. Journal of Business Ethics, 139(2), 391–409.

Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. Retrieved from

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, 23–32.

Kara, E., Uğurlu, M., & Körpi, 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.

Lau, C. K., & Ooi, K. W. (2016). A case study on fraudulent financial reporting: Evidence from Malaysia. Accounting Research Journal, 29(1).

Lokanan, M. E. (2017). A fraud investigation plan for a false accounting and theft case Journal of Financial Crime. Journal of Financial Crime.

Maccarthy, J. (2017). Using Altman Z-score and Beneish M-score Models to detect financial fraud and corporate failure : A case study of Enron Corporation. International Journal of Finance and Accounting, 6(6), 159–166.

Mahama, M. (2015). Detecting corporate fraud and financial distress using the Altman and Beneish Models. International Journal of Economics, Commerce and Management, III(1), 1–18.

Mohamed Sadique, R. B., Roudaki, J., Clark, M. B., & Alias, N. (2010). Corporate fraud: An analysis of Malaysian Securities Commission Enforcement Releases. International Journal of Social, Human Sciences and Engineering, 4(6), 1213–1222. Retrieved from

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, 5(2), 2–4.

Özcan, A. (2018). The use of Beneish Model in forensic accounting : Evidence from Turkey. Journal of Applied Economics and 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.

Petrík, V. (2016). Application of Beneish M-Score on Selected Financial Statements. (December).

Prevoo, L. J. B. (2007). Detecting earnings management: a critical assessment of the Beneish Model. Student International Business, (1–9).

PricewaterhouseCoopers. (2016). Economic Crime from the Board to the Ground: Why a Disconnect is Putting Malaysian Companies at Risk. Global Economic Crime Survey (Malaysia Report).

PricewaterhouseCoopers. (2018). PwC ’ s Global Economic Crime and Fraud Survey 2018: Southeast Asia Report. Retrieved from

Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063–1073.

Rezaee, Z. (2005). Causes, consequences, and deterrence of financial statement Accounting, fraud. Critical Perspectives on Accounting, 16(3), 277-298.

Roy, C., & Debnath, P. (2015). Earnings management practices in financial reporting of Public enterprises in India : An empirical test with M-Score.

Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42, 335–370.

Salehi, M., & Azary, Z. (2008). Fraud detection and audit expectation gap: Empirical evidence from Iranian bankers. International Journal of Business and Management, 3(10), 66–77.

Securities Commission Malaysia. (2019). Enforcement-Analytics. Retrieved May 17, 2019, from

Sekaran, U., & Bougie, R. (2013). Research Methods for Business (6th ed.). United Kingdom: John Wiley & sons Ltd.

Shanmugam, B., Nair, M., & Suganthi, R. (2010). Money laundering in Malaysia. Journal of Money Laundering Control, 6(4), 373–378.

Tarjo, & Herawati, N. (2015). Application of Beneish M-Score Models and data mining to detect financial fraud. Procedia - Social and Behavioral Sciences, 211, 924–930.

Tibbs, S. L. (2003). The ability of earnings management models to detect and predict public discovery of accounting-fraud. (Doctoral dissertation). Retrieved from ProQuest Information and Learning Company. (UMI 3119312)

Wan Mohd Razali, W. A. A., & Arshad, R. (2014). Disclosure of corporate governance structure and the likelihood of fraudulent financial reporting. Procedia - Social and Behavioral Sciences, 145, 243–253.

Warshavsky, M. (2012). Analyzing earnings quality as a financial forensic tool. Financial Valuation and Litigation Expert Journal, 39(16–20).

West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers and Security, Vol. 57.

Young, S. M., & Peng, E. Y. (2013). An analysis of accounting frauds and the timing of analyst coverage decisions and recommendation revisions: Evidence from the US. Journal of Business Finance and Accounting, 40(3–4), 399–437.


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