On Evaluating Likely Misstatements in Financial Statements of Economic Entities

The article presents and discusses the results of the research regarding the identification of the user groups of financial statements (internal and/or external). Possible misstatements of financial reporting are aimed at developing the ≪desired≫ behavior of these groups. The authors introduced the following theoretical, methodological, instrumental and informational constraints of the carried-out experiment: financial reporting was postulated as an open system of economic measurement; accounting ≪paradoxes≫, formulated and considered by Sokolov Ya.V., were used as the theoretical basis of the a priori probability of existence of inaccurate accounting data in the information space; the formation of learning samples and the general population was carried based on the reporting data on certain business activities. Concurrent fulfillment of these conditions ensured the correct application of the central limit theorem, resulting in a valid statistical conclusion. The ≪paradoxes≫ were divided into complementary pairs with their subsequent formalization through the relevant financial ratios. Actual series of the financial ratios’ distribution were compiled based on the existent populations. The significance of deviation of the obtained series from the normal probability distribution (Gaussian distribution) was estimated. The multimodality of series of the financial ratios distribution was identified and estimated, suggesting the possibility of the existence of intentional, systematic registration errors, and, as a result, presumable manipulation in accounting (financial) reporting. It was found that some economic entities knowingly tamper with financial results, effectively aggravating the ≪paradoxical≫ state. The directions of probable manipulation were revealed, making it possible to define the user groups, whose professional judgment and decisions are affected by the presumed misstatements. The SKRIN database (financial and accounting reporting of 10.0 thousand of food and beverage manufacturers) and government statistics were used as a basis for research; calculations were made using Statistica12 software.

1. Golitsyn G.A., Petrov V.M. Social and Cultural Dynamics: Long-Term Trends (Informational Approach). Moscow: KomKniga Publ.; 2005. 272 p. (In Russ.)

2. Thomas T.L. Reflexive Control in Russia: Theory and Military Applications. (In Russ.) Available from URL: http://www.intelros.ru/pdf/stratagemi/Tomas.pdf (accessed: 15.03.2019).

3. Lefebvre V.A., Smolyan G.L. Algebra of conflict. Moscow: Publishing house «Knowledge»; 1968. 64 p. (In Russ.)

4. Leonenko S. Reflexive Control of the Enemy. Army collection. 1995;(8). 28 p. (In Russ.)

5. Zharenov A.B. Stratagems and Reflexive Control in the Military Sphere. Scientific and educational problems of civil protection. 2009;(2):82-85. (In Russ.)

6. Raskin A.V., Tarasov I.V. Reflexive Control as a Technology of Information Impact. Information wars. 2014;(2),15-17. (In Russ.)

7. Lefebvre V.A. Lectures on the Theory of Reflective Games. Moscow: Kogito-Center Publ.; 2009. 218 p. (In Russ.)

8. Danilov V., Sotskov A. Social Choice Mechanisms. Berlin: Springer-Verlag; 2002. 191 p.

9. Stahl D. Evolution of Smart-n Players. Games and Economic Behavior. 1993;(5):604-617.

10. Alekseev M.A. Information Space of the Financial Market: Monograph. Novosibirsk: NSUEM Publ.; 2017. 247 p. (In Russ.)

11. Diekmann A. Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data. Journal of Applied Statistics. 2007;(34):321-329.

12. Durtschi C., Hillison W., Pacini C. The Effective Use of Benford`s Low to Assist in Detecting Fraud in Accounting Data. Journal of Forensic Accounting. 2004;(5):17-34.

13. Benish M.D. The Detection of Earning Manipulation. Financial Analysts Journal. 1998;55(5):24-36.

14. Sloan R.G. Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings. The Accounting Review. 1996;71(3):283-315.

15. DeAngelo L. Accounting Numbers as a Market Value Substitute: A Study of Managerial Buyouts of Public Stockholders. The Accounting Review. 1986;(61):400-420.

16. Alekseev M.A., Glinskiy V.V., Likhutin P.N. Statistical Research of Financial Market Information Space. Voprosy statistiki. 2017;(5):28-38. (In Russ.)

17. Dossey P. Sold! Art and Money. Saint-Petersburg: Limbus Press Publ.; 2017. 288 р. (In Russ.)

18. Mulford Ch., Comiskey E. The Financial Numbers Game: Identifying Creative Accounting Practices. New York: John Wiley & Sons; 2002. 408 p.

19. Susmus T., Demirhan D. Creative Accounting: A Brief History and Conceptual Framework. In: Proceedings of 3rd Balkans and Middle East Countries Conference on Accounting and Accounting History. June 19-22, 2013. 20 p.

20. Ali Shah S.Z., Butt S., Bin Tariq Y. Use or Abuse of Creative Accounting Techniques. International Journal of Trade, Economics and Finance. 2011;(6):531-536.

21. Sokolov Ya.V. The Bases of Account Theory. Moscow: Finansy I Statistica Publ.; 2000. 496 p. (In Russ.)

22. Sokolov Ya.V., Pyatov M.L. True and Fair View upon the Financial Information. Accounting. 2007;(5):52-56. (In Russ.)

23. Mathews M.R., Perera M.H. Accounting theory & development. Nelson; 1996. (Russ. ed.: Met’yus M.R., Perera M.Kh.B.; Sokolov Ya.V., Smirnova I.A. (eds.) Teoriya bukhgalterskogo ucheta: ucheb. Moscow: Audit, YuNITI Publ.; 1999. 663p.)

24. Sher I.F. Accounting and Balance. Moscow: Ekonomika I Zhizn Publ., 1926. (In Russ.)

25. Erickson M., Hanlon M., Maydew E.L. Is There a Link Between Executive Equity Incentives and Accounting Fraud? Journal of Accounting Research. 2006;(44):113-143.

26. Hogan C.E., Rezaee Z., Riley R.A., Velury U. Financial Statement Fraud: Insights from the Academic Literature. AUDITING: A Journal of Practice and Theory. 2008;(27):231-252.

27. Kaminski K.A., Wetzel T.S. Financial Ratios and Fraud: An Exploratory Study Using Chaos Theory. Journal of Forensic Accounting. 2004;(5):147-172.

28. Easley D., O’Hara M. Information and the Cost of Capital. The Journal of Finance. 2004;4(59):1553-1583.

29. Alekseev M.A., Dudin S.A. Misstatement Detection Rate of Company’s Performance. Vestnik IPB (Vestnik of Professional Accountants). 2017;(6):36-48. (In Russ.)

30. Alekseev M.A., Tyuzhina M.S. Identification of the direction of distortion of financial statements. Siberian Financial School. 2016;(6):129-133. (In Russ.)

31. Savelyeva M.Y., Borodina Y.B., Kukhta A.O. The Research on the Use of Express-Analysis of Financial Indicators for Discovery of Potential Manipulation of Financial Reporting by Russian Corporations. Journal of Economy and entrepreneurship. 2016. No. 11-1(76-1). Р. 529-532. (In Russ.)

32. Glinskiy V., Serga L., Alekseev M., Samotoy N., Simonova E. The Development of the Food Industry as a Condition for Improving Russia’s National Security. Procedia Manufacturing. 2018;21:838-845.

33. Glinskiy V.V., Serga L.K., Pulyaevskaya V.L. Statistical Tools in Solving the Problems of Managing the Development of the Territories. Voprosy statistiki. 2014;(10):14-20. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2014-0-10-14-20.

34. Glinskiy V.V. Small Business Mythological Statistic. Problems of Turbulent Sets Study. ECO. 2008;3(9):51-62 (In Russ.)