USING Z-SCORE IN DETECTION OF REVENUE MANIPULATIONS

Purpose of the article is to report the additions to the algorithm of detection of revenue manipulation based on DuPont analysis used for evaluating various parts of Return on Equity and Altman Z-Score used to determine the state of liquidity of the organization under review. In this paper these approaches are combined in order to show that they can be important tools in investigation of potential revenue manipulations based on the publicly available financial statements Methodology used in this paper combines non-parametric statistical methods allowing to eliminate chance in observations and discriminant analysis, which is commonly used in investigation of two samples, which can be separated based on the known criterion (here the presence of fraud in the examined statements) Scientific Aim of this paper is to provide the science and the financial experts community with the tool of determining fraud in financial statements and the algorithm of determining if the occurrence of such fraud Findings By using the algorithms and methods presented here it was possible to separate the fraudulent and perceivable clean samples via the use of discriminant analysis and to prove that the companies which are deemed financially stable based on the Altman analysis provide a better sample for the research of revenue manipulations than the arbitrary sample of companies. It was also possible to establish a fraud criteria and show its usability based on one of the most prolific fraud cases (Enron). Conclusions This paper presents two most important findings: the criterion of the separation of manipulator vs. non-manipulators and the set of variables used, which would allow repeating this research under any modern GAAP