Mathematical model of regional industry management based on the analysis of its financial and economic indicators
Abstract
This article proposes a methodology that can be used as the basis for a reasonable software package that allows: 1) to assess the economic state of a given industry in the region based on open sources of financial data and fuzzy logic; 2) observation of correlation dependencies between factors based on methods of correlation analysis and systems of fuzzy-logical inferences; 3) fuzzy-cognitive analysis of the industry in order to form a management strategy. The proposed concept of forming a management policy strategy has been tested at IT enterprises in the Rostov region. The result of the simulation is a set of indicators for the formation of management measures, improving the financial and economic situation in the industry by groups of all enterprises. The proposed methodology is a set of mathematical models, algorithms and software tools for automatic control of detection under conditions of complete uncertainty as applied to the financial and economic situation in the industry. It can be chosen to solve problems in the field of economics, sociology, biology and requires certain knowledge related to the study of random processes and the choice of their control.
Full Text:
PDF (Russian)References
Artyukhova, A.V. & Litvin, A.A. (2015) Analysis of the financial condition of an enterprise: the essence and necessity of carrying out. Young Scholar, 11, 744-747.
Fursova, M. N, Ilyin A.A. & Moiseeva, L.V. (2011) Analysis of economic activity: textbook. Voronezh, Russia, VGUES Publishing House.
Hoarse, F.P. & Husky, A.F. (2012) Comparative analysis of methods for assessing the financial condition of an organization. Polythematic network electronic scientific journal of the Kuban State Agrarian University, 81, 22.
Kuvshinov M.S. (2012) Innovative tools for predicting the assessment of the financial condition of an enterprise. Bulletin of the South Ural State University, Series: Economics and Management, 30, 56
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 4, 589-609.
Altman, E. I. (1984). Further empirical investigation of the bankruptcy cost question. Journal of Finance, 39(4), 1067-1089.
Lis model. (n.d.). Retrieved from http://1-fin.ru/?id=281&t=967
Deakin, E. (1972). Discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167-179.
Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical modeling. Accounting & Business Research, 13(52), 295-307.
Springite Model. (n.d.). Retrieved from http://1-fin.ru/?id=281&t=1572
Fulmer Model. (n.d.). Retrieved from http://1-fin.ru/?id=281&t=1176
Saifullin Model. (n.d.). Retrieved from http://1-fin.ru/?id=281&t=1164
Zaitseva, O. P. (1998). Crisis management in Russian company. Aval, 11-12, 66-73.
Davydova, G. V. & Belikov, A. Yu. (1999). Methods of quantitative assessment of bankruptcy risk of enterprises. Risk Management., 3, 13-20.
Fedorova, E., Gilenko, E. & Dovzhenko, S. (2013). Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications, 18(40), 7285-7293.
Kochenev, Yu. Yu., Lukashevich, N.S. (2011). Assessment of audit risk based on fuzzy logic. Nauchno-technicheskie Vedomosti St. Petersburg State Polytechnic University. Economics, 6, 248-253.
Smelova, T.A., & Merzlikina G.S. (2003) Evaluation of economic solvency in anti-crisis management of an enterprise. Volgograd, Russia, VolgGTU.
Nedosekin, A. O. (2003). Fuzzy financial management. Moscow, Russia: AFA Library.
Nedosekin, A. O. (2000). Application of the fuzzy sets to the problems of financial management. Audit and financial analysis, Retrieved from https://www.cfin.ru/press/afa/2000-2/08.shtml
Nedosekin, A. O., Kozlovsky, A. N., Abdulaeva, Z. I. (2018). Analysis of branch economic stability by fuzzy-logical methods. Economics and management: problems, solutions, 5, 10-16.
Zade, L. A. (1976). Concept of a linguistic variable and its application to making approximate decisions. Moscow, USSR: Mir.
Yakimova, V. A. (2012). Optimization of audit actions on the basis of an assessment of sufficiency of auditor proofs and labor input of process of their collecting. International Accounting, 43, 25-36.
Nedosekin A. O. (2003) Financial management in vague conditions. (FUZZY FINANCIAL MANAGEMENT). Russia, Moscow, AFA Library.
Nedosekin A.O. (2005) Business risk assessment based on fuzzy data: Monograph. St. Petersburg, Russia.
Audit IT. (2022) Financial analysis. Audit firm "Avdeev and K": audit and accounting services, 1999 - 2019. Retrieved from https://www.audit-it.ru
TestFirm. Comparison of the financial condition of the company with industry indicators and competitors. www.testfirm.ru
Kramarov S.O., Ovsyannikov V.A., Sakharova L.V., Usatii R.S., Lukyanova G.V. Automated data collection of key financial indicators of enterprises in the IT industry in the region. Bulletin of Cybernetics. 2022. No. 3 (47). pp. 39-45.
Kramarov S.O., Arapova E.A., Sakharova L.V., Usatii R.S., Lukyanova G.V. Methodology for assessing the financial and economic state of the region's industry based on the algorithm of fuzzy-multiple aggregation of financial and economic indicators. Bulletin of SurSU. 2022. No. 3 (37). pp. 23-34.
Arapova E.A., Kramarov S.O., Usatii R.S., Rutta N.A., Sakharova L.V. Software implementation of fuzzy-multiple models for a comprehensive assessment of the dynamics of the financial and economic state of the industry. Bulletin of the Russian New University. Series: Complex systems: models, analysis and control. 2022. No. 3. S. 101-117.
Yager R.R. and J, Kacprzyk (Eds.) The Ordered Weighted Averaging operators. Theory and Applications, Kluwer Academic Publishers, USA, 1997.
Kuncheva L.I. "Fuzzy" vs "Non-fuzzy" in combining classifiers designed by boosting, IEEE Transactions on Fuzzy Systems, 11(6), 2003, pp. 729-741.
Amarante, M. (2018). Mm-OWA: A generalization of OWA operators. IEEE Transactions on Fuzzy Systems, 26(4), 2099–2106. doi:10.1109/TFUZZ.2017.2762637
Gong, C., Li, W., & Yi, P. (2019). Rank-based analysis method to determine OWA weights and its application in group decision making. International Journal of Intelligent Systems, 34(7), 1685–1699. doi:10.1002/int.22116
Beliakov, G., James, S., Wilkin, T., & Calvo, T. (2018). Robustifying OWA operators for aggregating data with outliers. IEEE Transactions on Fuzzy Systems, 26(4), 1823–1832. doi:10. 1109/TFUZZ.2017.2752861
Leite, D., & Skrjanc, I. (2019). Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction. Information Sciences, (504), 95–112. doi:10.1016/j.ins.2019. 07.053
Mesiar, R., Sipeky, L., Gupta, P., & LeSheng, J. (2018). Aggregation of OWA operators. IEEE Transactions on Fuzzy Systems, 26(1), 284–291. doi:10.1109/TFUZZ.2017.2654482
Nedosekin, A. O. (2003). Fuzzy financial management. Moscow, Russia: AFA Library.
Nedosekin, A. O. (2000). Application of the fuzzy sets to the problems of financial management. Audit and financial analysis, Retrieved from https://www.cfin.ru/press/afa/2000-2/08.shtml
Nedosekin, A. O., Kozlovsky, A. N., Abdulaeva, Z. I. (2018). Analysis of branch economic stability by fuzzy-logical methods. Economics and management: problems, solutions, 5, 10-16.
Financial director. https://www.fd.ru/question/1814-kak-povysit-rentabelnost-aktivov.
SME Corporation. Federal Corporation for the Development of Small and Medium Enterprises. https://corpmsp.ru/finansovaya-podderzhka/zontichnyy-mekhanizm-predostavleniya-poruchitelstv/
My business. Portal for supporting small and medium-sized businesses. https://xn--90aifddrld7a.xn--p1ai/
Avdeeva Z.K., Kovriga S.V., Makarenko D.I. Cognitive modeling for solving problems of control of semi-structured systems (situations) // Management of large systems. 2007. Issue. 16. S. 26-39.
Kondrashina O.N., Anokhina M.E. The use of fuzzy cognitive maps in assessing the quality of economic growth in a particular industry // Economics and Entrepreneurship. 2017. No. 5-1. pp. 896-899.
Podgorskaya S.V., Podvesovsky A.G., Isaev R.A., Antonova N.I. Construction of Fuzzy Cognitive Models of Socio-Economic Systems on the Example of a Management Model for the Integrated Development of Rural Territories // Business Informatics. 2019. V. 13. No. 3. P. 7
Silov V.B. Making strategic decisions in a fuzzy environment. Moscow: INPRO-RES. 1995. 228 p.
Spodareva E.G., Kuzmina T.S. Application of correlation-regression analysis to assess the financial sustainability of an enterprise // Bulletin of the Ural Institute of Economics, Management and Law. 2020. No. 4 (53).
Safaryan S.A. Development of a financial condition model, forecasting based on multiple regression // Economics and business: theory and practice. 2020. 12-3.
Yudkina L.V., Berlin Yu.I. Correlation analysis of interrelations between indicators of capitalization dynamics and performance efficiency of public Russian companies // Finance and credit. 2009. No. 9 (345).
Savelyeva M.Yu., Majorko E.A., Vagaitseva V.P. Study of the relationship between financial ratios in the bankruptcy forecasting models of E. Altman and IGEA // Science, technology and education. 2017. No. 1 (31).
Tarasova A.Yu. Investigation of functional relationships of financial indicators calculated according to the methodology approved by the FSFO // ECONOMINFO. 2004.
Denis, J. D. (2001), The origins of correlation and regression: Francis Galton or Auguste Bravais and the error theorists?, History and Philosophy of Psychology Bulletin, 13, pp. 36-44.
Gogtay, N. J. and Thatte, U. M. (2017), Principles of correlation Analysis, Journal of The Association of Physicians of India, 65 (March), pp. 78-81.
Glen, S. (2015), Multicollinearity: Definition, Causes, Examples,https://www.statisticshowto.datasciencecentral.com/multicollinearity/, Retrieved: 30-05-2019.
Hauke, J. and Kossowski, T. (2011), Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data, Questiones Geographicae, 30(2), pp. 87-93 (doi: http://dx.doi.org/10.2478/v10117-011-0021-1). https://ssrn.com/abstract=3416918
Senthilnathan, S. (2017), Relationships and Hypotheses in Social Science Research, https://ssrn.com/abstract=3032284 or http://dx.doi.org/10.2139/ssrn.3032284, Retrieved: 09- 07-2019,
Smith, M. D., Handshoe, R., Handshoe, S., Kwan, O. L., and Demaria, A. N. (1986), Comparative accuracy of two-dimensional echocardiography and Doppler pressure half-time methods in assessing severity of mitral stenosis in patients with and without prior commissurotomy, Circulation, 73(1)-Jan, pp. 100-107.
Kalyanov G.N. Conceptual model of DFD-technology // Open education. 2017. No. 4.
Podvesovsky A.G., Lagerev D.G., Korostelev D.A. DSS "IGLA". (Certificate of the branch fund of algorithms and programs of Rosstat No. 50200701348). 2018. URL: http://iipo.tu-bryansk.ru/quill/developers.html.
Refbacks
- There are currently no refbacks.
Abava Кибербезопасность IT Congress 2024
ISSN: 2307-8162