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Fuzzy-Logical Expert System For Assessing The Financial Security Of Enterprises

Author

Listed:
  • Valentin Myachin

    (Ukrainian State University of Chemical Technology, Ukraine)

  • Olena Yudina

    (Private Institution of Higher Education "Dniprovskii University of the Humanities", Ukraine)

  • Oleksandr Myroshnychenko

    (Ukrainian State University of Chemical Technology, Ukraine)

Abstract

The purpose of this study is to build a fuzzy expert system for assessing the financial component of the economic security of telecommunications enterprises. The methodological basis of the research is founded on scientific works of domestic and foreign scientists and leading experts in the field of financial analysis and modeling of economic processes, as well as statistical and financial reporting data that are publicly available. To construct an integral indicator of the financial security of an enterprise, a fuzzy conclusion is used. Three financial indicators are used as input variables. The first indicator X1 is the Current Ratio (CR). The second indicator X2 is Equity Ratio (ER). The third indicator is Return on Assets (ROA). The output variable is defined as an indicator of the financial security of an enterprise Y123 (FS). Both the input variables and the output variable are converted to fuzziness by constructing membership functions. The type and parameters of the affiliation function are justified, and the bell-shaped affiliation function is chosen to describe the uncertainty of values that fall under the normal distribution. The quantity of fuzzy sets at every input is considered as z=3 and the quantity of input variables is considered as ω=3. To achieve completeness of the model, the quantity of logic rules is considered as r=33=9. To calculate a degree of market concentration, Mamdani fuzzy conclusion is applied. Defuzzification is engaged to calculate the value of the output variable Y123(FS) for an indicator that determines the degree of financial security of an enterprise and, as a result, the degree of its economic security. To assess the level of the financial security indicator of an enterprise, a fuzzy expert system is constructed. The fuzzy expert system allows you to use various indicators thanks to the fuzzy logic methodology, which takes into account the fuzziness of input variables and output variables as much as possible. For the three telecommunications companies whose core business is wireline communication, ratios are calculated based on financial reports. Financial coefficients are used to determine the integral indicator of financial security of enterprises. This indicator can be characterized by both numerical values and linguistic terms.

Suggested Citation

  • Valentin Myachin & Olena Yudina & Oleksandr Myroshnychenko, 2021. "Fuzzy-Logical Expert System For Assessing The Financial Security Of Enterprises," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 7(4).
    • Handle: RePEc:bal:journl:2256-0742:2021:7:4:15
    DOI: 10.30525/2256-0742/2021-7-4-123-135
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    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    financial security of the enterprise; integral indicator; fuzzy expert system; fuzzy logic; membership function; defuzzification; Current Ratio (CR); Equity Ratio (ER); Return on Assets (ROA);
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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