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Igor Pustylnick ()
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Igor Pustylnick: Far Eastern Federal University, Russian Federation

Oeconomia Copernicana, 2017, vol. 8, issue 1, 83-97

Abstract: 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/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.

Keywords: earnings management; financial statement fraud; revenue manipulations; M-Score; Z-Score (search for similar items in EconPapers)
JEL-codes: M40 M41 M42 M48 (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:pes:ieroec:v:8:y:2017:i:1:p:83-97