Determining the Level of Accounting Conservatism through the Fuzzy Logic System

Open access

Abstract

Background: Using a variety of alternative accounting policies brings about different effects on the stated business results and the value of the company. Objectives: The objective of this paper is to develop a fuzzy logic solution for determining bias in financial reports on low-activity financial markets, and to find a method applicable to unquoted entities. Methods/Approach: A fuzzy logic system was developed using data on Croatian companies, the MatLab software, and the Mamdani fuzzy inference method. Results: The paper provides the summary of results obtained using a fuzzy logic system, and they indicate that the model has relevant validity. Conclusions: The model can serve as a stimulus for more detailed studies of biased financial statements elements. The fuzzy logic model should be further tested on a larger sample of companies classified based on their activity and under different business conditions.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Ahmed A. S. Duellman S. (2013) “Managerial Overconfidence and Accounting Conservatism” Journal of Accounting Research Vol. 51 No. 1 pp. 1-30.

  • 2. Ahmed A. S. Duellman S. (2007) “Accounting conservatism and board of director characteristics: An empirical analysis” Journal of Accounting and Economics Vol. 43 No. 2-3 pp. 411-437.

  • 3. Ahmed A. S. Bilings K. B. Morton. R. M. Stanford-Harris M. (2002) “The role of accounting conservatism in mitigating bondholder-shareholder conflicts over dividend policy and in reducing debt costs” The Accounting Review Vol. 77 No. 4 pp. 867-890.

  • 4. Alcalá-Fdez J. Alonso J. M. (2016) “A Survey of Fuzzy Systems Software: Taxonomy Current Research Trends and Prospects” IEEE Transactions on Fuzzy Systems Vol. 24 No. 1 pp. 40-56.

  • 5. André P. Filip A. Paugam L. (2015) “The effect of mandatory IFRS adoption on conditional conservatism in Europe” Journal of Business Finance and Accounting Vol. 42 No. 3-4 pp. 482–514.

  • 6. Beskese A. Kahraman C. Irani Z. (2004) “Quantification of flexibility in advanced manufacturing systems using fuzzy concept” International Journal of Production Economics Vol. 89 No. 1 pp. 45-56.

  • 7. Balakrishnan K. Watts R. L. Zuo L. (2016) “The Effect of Accounting Conservatism on Corporate Investment during the Global Financial Crisis” Journal of Business Finance and Accounting Vol. 43 No. 5-6 pp 513-542.

  • 8. Ball R. Shivakumar L. (2005) “Earnings quality in U.K. private firms: Comparative loss recognition timeliness” Journal of Accounting and Economics Vol. 39 No. 1 pp. 83-128.

  • 9. Barker R. McGeachin A. (2015) “An Analysis of Concepts and Evidence on the Question of Whether IFRS Should be Conservative” ABACUS Vol. 51 No. 2 pp. 169-207.

  • 10. Barker R. McGeachin A. (2013) “Why is there inconsistency in accounting for liabilities in IFRS? An analysis of recognition measurement estimation and conservatism” Accounting and Business Research Vol. 43 No. 6 pp. 579-604.

  • 11. Basu S. (1997) “The conservatism principle and the asymmetric timeliness of earnings” Journal of Accounting and Economics Vol. 24 No. 1 pp. 3-37.

  • 12. Beatty A. Liao S. (2011) “Do delays in expected loss recognition affect banks’ willingness to lend?” Journal of Accounting and Economics Vol. 52 No. 1 pp. 1-20.

  • 13. Beatty A. Petacchi R. Zhang H. (2012) “Hedge commitments and agency costs of debt: Evidence from interest rate protection covenants and accounting conservatism” Review of Accounting Studies Vol. 17 No. 3 pp. 700-738.

  • 14. Beaver W. H. Landsman W. R. Owen E. L. (2012) “Asymmetry in Earnings Timeliness and Persistence: A Simultaneous Equations Approach” Review of Accounting Studies Vol. 17 No. 4 pp. 781-806.

  • 15. Chai Y. Jia L. Zhang Z. (2009) “Mamdani model based adaptive neural fuzzy inference system and its application” International Journal of Computational Intelligence Vol. 5 No. 1 pp. 22-29.

  • 16. Čičak J. (2018) “Fair Value of Financial Statement Elements” University of Rijeka Faculty of Economics and Business doctoral dissertation.

  • 17. Díaz B. Morillas A. (2012) “Some Experiences Applying Fuzzy Logic to Economics” in Seising R. Sanz Gonzalez V. (Eds.) Soft Computing in Humanities and Social Sciences Springer Berlin Heidelberg pp 347-379.

  • 18. Dietrich J. R. Muller K. A. Riedl E. J. (2007) “Asymmetric timeliness tests of accounting conservatism” Review of Accounting Studies Vol. 12 No. 1 pp. 95-124.

  • 19. Edelman D. Nicholson A. (2011) “Arthur Anderson Auditors and Enron: What happened to their Texas CPA licenses?” Journal of Finance and Accountancy Vol. 8 pp. 1-9.

  • 20. Ettredge M. Huang Y. Zhang W. (2012) “Earnings restatements and differential timeliness of accounting conservatism” Journal of Accounting and Economics Vol. 53 No. 3 pp. 489-503.

  • 21. Gao H. Huang J. (2018) “Employee Firing Costs and Accounting Conservatism: Evidence From Wrongful Discharge Laws” available at SSRN.

  • 22. Garcia Lara J. M. Garcia Osma B. Penalva F. (2016) “Accounting conservatism and firm investment efficiency” Journal of Accounting and Economics Vol. 61 No. 1 pp. 221-238.

  • 23. Garcia Lara J. M. Garcia Osma B. Penalva F. (2009) “Accounting conservatism and corporate governance” Review of Accounting Studies Vol. 14 No. 1 pp. 161-201.

  • 24. Giovanis E. (2010) “A study of panel logit model and adaptive neuro-fuzzy inference system in the prediction of financial distress periods” World Academy of Science Engineering and Technology Vol. 64 pp. 646-652.

  • 25. Givoly D. Hayn C. (2000) “The changing time-series properties of earnings cash flows and accruals: Has financial reporting become more conservative?” Journal of Accounting & Economics Vol. 29 No. 3 pp. 287-320.

  • 26. Huijgen C. Lubberink M. (2005) “Earnings conservatism litigation and contracting: The case of cross-listed firms” Journal of Business Finance & Accounting Vol. 32m No. 7-8 pp. 1275-1309.

  • 27. International Accounting Standards Board (2016) International Financial Reporting Standards – Red Book London IFRS Foundation.

  • 28. Kaur A. Kaur A. (2012) “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System” International Journal of Soft Computing and Engineering Vol. 2 No. 2 pp. 323-325.

  • 29. Khan M. Watts R. L. (2009) “Estimation and empirical properties of a firm-year measure of accounting conservatism” Journal of Accounting Economics Vol. 48 No. 2-3 pp. 132-150.

  • 30. Kim S. M. Kim S. M. Lee D. H. Yoo S. W.(2019) “How Investors Perceive Mandatory Audit Firm Rotation in Korea” Sustainability Vol. 11 No. 4.

  • 31. Kim Y. Li S. Pan C. Zuo L. (2013) “The role of accounting conservatism in the equity market: Evidence from seasoned equity offerings” The Accounting Review Vol. 88 No. 4 pp. 1327-1356.

  • 32. Lee E. Wang K. Wang Q. Zhang X. (2018) “Unintended Consequences of Economic Interventionism on Accounting Conservatism: A Natural Experiment” available at SSRN.

  • 33. Lee C. C. (1990) “Fuzzy logic in control systems: fuzzy logic controller. II” IEEE Transactions on systems man and cybernetics Vol. 20 No. 2 pp. 419-435.

  • 34. Lin J. W. Hwang M. I. Becker J. D. (2003) “A fuzzy neural network for assessing the risk of fraudulent financial reporting” Managerial Auditing Journal Vol. 18 No. 8 pp. 657-665.

  • 35. Nikolaev V. (2010) “Debt Covenants and Accounting Conservatism” Journal of Accounting Research Vol. 48 No. 1 pp. 51-89.

  • 36. Penman S. H. Zhang X. J. (2002) “Accounting conservatism and the quality of earnings and stock returns” The Accounting Review Vol. 77 No. 2 pp. 237-264.

  • 37. Pratt J. (2008) Financial Accounting in an Economic Context John Wiley & Sons.

  • 38. Qu X. Zhang G. (2010) “Measuring the convergence of national accounting standards with international financial reporting standards: The application of fuzzy clustering analysis” The International Journal of Accounting Vol. 45 No. 3 pp. 334-355.

  • 39. Ruch G. W. Taylor G. (2015) “Accounting conservatism: A review of the literature” Journal of Accounting Literature Vol. 34 pp. 17-38.

  • 40. Solomons D. (1986) Making Accounting Policy: The quest for credibility in financial reporting New York Oxford University Press.

  • 41. Spandagos C. Ng T. L. (2018) “Fuzzy model of residential energy decision-making considering behavioral economic concepts” Applied Energy Vol. 213 pp. 611-625.

  • 42. Tan L. (2013) “Creditor control rights state of nature verification and financial reporting conservatism” Journal of Accounting and Economics Vol. 55 No. 1 pp. 1-22.

  • 43. Zhang J. (2008) “The contracting benefits of accounting conservatism to lenders and borrowers” Journal of Accounting and Economics Vol. 45 No. 1 pp. 27-54.

  • 44. Wang R. Z. Ó Hogartaigh C. van Zijl T. (2009) “A Signaling Theory of Accounting Conservatism” Journal of accounting literature Vol. 28. pp. 165-203.

  • 45. Wang C. Xie F. Xin X. (2018) “CEO Inside Debt and Accounting Conservatism” Contemporary Accounting Research Vol. 35 No. 4 pp. 2131-2159.

  • 46. Watts R. L. (2003a) “Conservatism in Accounting Part I: Explanations and Implications” Accounting Horizons Vol. 17 No. 3 pp. 207-221.

  • 47. Watts R. L. (2003b) “Conservatism in Accounting Part II: Evidence and Research Opportunities” Accounting Horizons Vol. 17 No. 4 pp. 287-301.

Search
Journal information
Impact Factor


CiteScore 2018: 0.57

SCImago Journal Rank (SJR) 2018: 0.165
Source Normalized Impact per Paper (SNIP) 2018: 0.388

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 183 183 36
PDF Downloads 167 167 24