Main Article Content
Research background: Since the turn of the 21st century financial statement manipulations became the center of attention for accountants, auditors and financial analysts. Since being classified by the regulators as fraudulent, earnings management has required a separate detection methodology. The majority of detection research is performed through the comparison of a large number of statements for the same company in order to find irregularities in earnings behavior. Shortening of the detection time and the amount of data becomes important.
Purpose of the article: The goal was to compare the characteristics of M-Score and ∆P-∆R and to find their advantages and limitations. Applying both indicators to the different samples, the research attempted to determine the statistical connection between them and to set up the limits of their applicability. Since M-Score indicator is liquidity-based, this research attempted to determine to which extent M-Score and Z-Score are statistically related.
Methods: The research paper compares the behavior of both indicators using various samples of financial data: the sample of companies, charged with fraud, the sample with exceptional liquidity, the large random sample and the sample from the emerging market economy. Based on the original observations, two other subsamples (one based on poor Z-Score and one based on exceptional Z-Score) were extracted from the main sample. For all samples ∆P-∆R, M-Score and Z-Score were statistically compared among and between themselves.
Findings and value added: The research found the limitations of ∆P-∆R and M-Score in the stable markets and was able to connect them in the emerging market by using linear regression model (also including Z-Score). The research confirmed that M-Score can mistake exceptional performance for manipulations, resulted in Type I errors. ∆P-∆R appeared somewhat coarse and prone to Type II errors. The combined use of both in the emerging markets will provide the best approach.
Beneish, M. (1997). Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16(3). doi: 10.1016/S0278-4254(97)00023-9.
Bruns, W. J., & Merchant, K. A. (1990). The dangerous morality of managing earnings. Management Accounting, 72(2).
Chychyla, R., & Kogan, A. (2013). Using XBRL to conduct a large-scale study of discrepancies between the accounting numbers in compustat and SEC 10-K filings. Retrieved form http://papers.ssrn.com/sol3/papers.cfm?abstract_ id=2304473.
Cormier, D., & Martinez, I. (2006). The association between management earnings forecasts, earnings management, and stock market valuation: evidence from French IPOs. International Journal of Accounting, 41(3). doi: 10.1016/j.intacc.2006.07.004.
Das, R. C., Mishra, C. S., & Rajib, P. (2016). Detection of anomalies in accounting data using Benford’s law: evidence from India. Journal of Social Science Studies, 4(1). doi: 10.5296/jsss.v4i1.9873.
Dechow, P. M., & Skinner, D. J. (2000). Earnings management: reconciling the views of accounting academics, practitioners and regulators. Accounting Horizons, 14(2). doi: 10.2308/acch.2000.14.2.235.
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. Accounting Review, 70(2).
Dyreng, S. D., Hanlon, M., & Maydew, E. L. (2010). The effects of executives on corporate tax avoidance. Accounting Review, 85(4). doi: 10.2308/accr.2010. 85.4.1163.
Escalada, J. (2011). 45 dividend stocks with good credit scores. Retrieved form from http://seekingalpha.com/article/307563-45-dividend-stocks-with-good-credit-scores.
Fich, E. M., & Shivdasani, A. (2007). Financial fraud, director reputation, and shareholder wealth. Journal of Financial Economics, 86(2). doi: 10.1016/j.jfineco.2006.05.012.
Franceschetti, B. M., & Koschtial, C. (2013). Do bankrupt companies manipulate earnings more than the non-bankrupt ones? Journal of Finance and Accountancy, 12.
Healy, P. (1985). The effects of bonus schemes on the accounting decisions. Journal of Accounting and Economics, 7. doi: 10.1016/0165-4101(85)90029-1.
Jansen, I. P., Ramnath, S., & Yohn, T. L. (2012). A diagnostic for earnings management using changes in asset turnover and profit margin. Contemporary Accounting Research, 29(1). doi: 10.1111/j.1911-3846.2011.01093.x.
Jones, J. J. (1991). Earnings management following import relief investigations. Journal of Accounting Research, 29(2). doi: 10.2307/2491047.
Marzcewski, D. C., & Akers, M. D. (2005). CPA’s perception on the impact of SAS 99. CPA Journal, 75(6).
McKee, T. E. (2005). Earnings management: an executive perspective. Cengage Learning.
Pustylnick, I. (2016). Using Z-Score in detection of revenue manipulations. Paper presented at the 21-st International Scientific Conference Economics and Management, Brno, Czech Republic. Retrieved form http://icem.lt/public/icem /ICEM_2016.pdf.
Pustylnik, E. I. (1968). Statistical methods of analisys and processing of observations. Moscow: Nauka Publishing.
San Miguel, J. G. (1977). The reliability of R&D data in COMPUSTAT and 10-K reports. Accounting Review, 52(3).
Shette, R., Kuntluru, S., & Korivi, S. R. (2016). Opportunistic earnings management during initial public offerings: evidence from India. Review of Accounting and Finance, 15(3). doi: 10.1108/RAF-03-2015-0048.
Tarjo, N. H. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Paper presented at the 2nd Global Conference on Business and Social Sciences (GCBSS-2015) on “Multidisciplinary Perspectives on Management and Society”, Bali, Indonesia.