Detecting the Risk of Manipulation of Financial Statements for Companies on the Bucharest Stock Exchange Applying the Beneish Model
In order to protect the interests of the stakeholders regarding the economic performance of the company, econometric models have been developed in the specialized literature that can determine the possibility of manipulating financial statements. The Beneish model developed by Professor Messod Beneish calculates a score based on eight financial rates. In Romania, based on the average score calculated for 1998-2017, 55% of companies listed on the regulated market of Bucharest Stock Exchange are likely to be manipulators. The result is worrying compared to that obtained in Italy for 2009-2010 and Greece for 2011-2012 (33%), or for Vietnamese companies during 2013-2014 (48.4%), but less dramatic than in Albania (68%). By analyzing the M-scores for each company, we conclude that 67% of the analyzed companies manipulated at least half of the annual financial statements, and of these 19% companies manipulated over 75% of the annual financial statements. The result is meant to sound an alarm for the government, even if there is also an optimistic evolution of the manipulators in the second part of the analyzed period and, in general, a linear, decreasing trend. Financial performance looks better in the manipulated financial statements (average profits, ROA, liquidity and solvency rates are higher, and the degree of indebtedness is lower), but it is a distorted performance, not a real one. The individual results, for each company separately, are of real interest to investors, auditors and other stakeholders of the real performance of the company.
 Anh NH, & Linh, NH. Using the M-score Model in Detecting Earnings Management-Evidence from Non-Financial Vietnamese Listed Companies, VNU Journal of Science, Economics and Business; 2016, 32 (2).
 Beneish MD. The Detection of Earnings Manipulation. Financial Analysts Journal, 1999. 55(5): 24–36. doi:10.2469/faj.v55.n5.2296
 Beneish, MD. Earnings management: a perspective. Managerial Finance, 2001, 27(12), 3–17.doi:10.1108/03074350110767411
 Beneish MD, Lee CMC, Nichols DC. Earnings manipulation and expected returns. Financial Analysts Journal, 2013, 69(2): 57-82
 Corsi C, Berardino D, Cimbrini T. Beneish M-score and detection of earnings management in Italian SMEs. Ratio Mathematica. Teramo, 2015, 28, 65-83.
 Cynthia H. Analysis ratios for detecting Financial Statement fraud. ACFE Fraud Magazine; 2005.
 MacCarthy J. 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, 2017, 6(6), 159-166.
 Nwoye UJ, Okoye I, Oraka AO. Beneish Model as effective complement to the application of SAS No. 99 in the conduct of Audit in Nigeria. Management and Administrative Sciences Review, 2013, 2(6): 640-655.
 Omar N, Koya RK., Sanusi ZM, Shafie NA. Financial statement fraud: A case examination using Beneish Model and Ratio Analysis. International Journal of Trade, Economics and Finance, 2014, 5(2).
 Petrik V. Application of Beneish M-score on selected financial statement. Košická bezpečnostná revue, 2016, 2: 307-312
 Repousis S. Using Beneish model to detect corporate financial statement fraud in Greece, Journal of Financial Crime, 2016, 23 (4): 1063-1073. Available from: https://doi.org/10.1108/JFC-11-2014-0055
 Robu IB, Robu MA. Proceduri de audit pentru estimarea riscului de frauda bazate pe indici de detectare a manipularilor contabile/Audit Procedures for Estimating the Fraud Risk Based on Indexes for Detection of Accounting Manipulation. Audit Financiar, 2013, 11(10), 3.
 Schuetze HG. Individual learning accounts and other models of financing lifelong learning. International Journal of Lifelong Education, 2007, 26(1): 5-23
 Shehu I. Financial Statement Manipulation. The Application Of Beneish M-Score Model For Detecting Accounting Fraud In Economic Units In Albania. International Journal Scientific Papers; 2015, 11 (2): 126-130.
 Tarjo & Herawati N. Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia - Social and Behavioral Sciences; 2015, 211: 924–930. doi:10.1016/j.sbspro.2015.11.122
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