Determining the Level of Accounting Conservatism through the Fuzzy Logic System
Čičak Josip and
Vašiček Davor
Additional contact information
Čičak Josip: Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia
Vašiček Davor: Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia
Business Systems Research, 2019, vol. 10, issue 1, 88-101
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.
Keywords: accounting conservatism; aggressive accounting; fuzzy logic (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.2478/bsrj-2019-0007 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:10:y:2019:i:1:p:88-101:n:7
DOI: 10.2478/bsrj-2019-0007
Access Statistics for this article
Business Systems Research is currently edited by Mirjana Pejić Bach
More articles in Business Systems Research from Sciendo
Bibliographic data for series maintained by Peter Golla ().