Improving the Altman Method for Assess the Creditworthiness of Enterprises with Economic Indicators in the Form of Fuzzy Numbers
Engineering Mathematics
Volume 4, Issue 1, June 2020, Pages: 10-13
Received: Aug. 13, 2019; Accepted: Sep. 6, 2019; Published: May 28, 2020
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Alevtina Shatalova, Department of Applied Mathematics, Kuban State University, Krasnodar, Russia
Konstantin Lebedev, Department of Applied Mathematics, Kuban State University, Krasnodar, Russia
Igor Shevchenko, Faculty of Economics, Кuban State University, Krasnodar, Russia
Boureima Bamadio, Faculty of Economics and Management, University of Social Sciences and Management of Bamako, Mali, Bamako
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The article describes the Altman Five-factor Model to assess the creditworthiness of the enterprise with the apparatus of the theory of fuzzy sets. There were two improvements. The early method used the square integral approximation for the accurately calculating of the quantitative assessment of creditworthiness and the apparatus of fuzzy sets for ordering the sets according to the degree of confidence of the probability obtained. The new method described in this article is expanded by presenting the input data as triangular fuzzy numbers. This article describes the simulation of the credit assessment procedure and the possibility of functioning of the model as well. This approach helps to adequately assess the creditworthiness of the enterprise, also to make it possible to predict the change in the result of the model due to possible errors in the input data. The results were tested at the Krasnodar cryptic plant.
Assessment of the Creditworthiness, Altman Model, Fuzzy Sets, Membership Function, Simulation, Decision-making Under Uncertainty, Errors in the Financial Statements
To cite this article
Alevtina Shatalova, Konstantin Lebedev, Igor Shevchenko, Boureima Bamadio, Improving the Altman Method for Assess the Creditworthiness of Enterprises with Economic Indicators in the Form of Fuzzy Numbers, Engineering Mathematics. Vol. 4, No. 1, 2020, pp. 10-13. doi: 10.11648/j.engmath.20200401.12
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Baranovskaya T. P., Kovalenko A. V., Urtenov M. H., Karmazin V. N. (2009); Modern mathematical methods of analysis of financial and economic condition of the enterprise; monograph. Kubsau.
Bakhvalov N. S. Numerical methods / H. S. Bakhvalov, N. P. Zhidkov, G. M. Kobelkov.-Moscow; Science, 1999.-630 p.
Davydova G. V., Belikov A. Yu. (1999); Methods of quantitative risk assessment of banking enterprises //risk Management. No. 3.
Diligenskii N., V., Dymova L. G., Sevastianov P. V. (2004); Fuzzy modelling and multi-objective optimization of production systems in conditions of uncertainty; Technology, Economy, Ecology M.; "Publishing house engineering−1".
Dontsova, L. V. (2006); Analysis of financial statements. Nikiforova.-4th ed., re-slave. and DOP. – M.; Business and Service.
Zade L. (1976); the Concept of a linguistic variable and its application to approximate decision-making. M.; Mir.
Zaitseva O. P. (1998); Crisis management in a Russian company//Siberian financial school. № 11-12.
Ibrahimov. VA (2010); Elements of fuzzy mathematics. Baku, ASOA.
Kovalenko A. V. (2009); Mathematical models and tools for complex assessment of financial and economic condition of the enterprise; Dis. kand. Econ. Sciences; 06.03.2009//Kuban state agrarian University.–Krasnodar.
Konysheva L. K., Nazarov D. M. (2011); fundamentals of the theory of fuzzy sets. St. Petersburg; PI-Ter.
Kofman A., Aluja H. Hill. (1992); Introduction of fuzzy set theory in enterprise management. Minsk; Higher school.
Kuznetsov L. A., Carriers A. V. (2008); Assessment of credit history of individuals on the basis of fuzzy models//Management of large systems. Issue 21. M.; IPU ran.
The Nedosekin A. O. (2004); Methodological foundations of modeling of financial activity with use of is indistinct-plural descriptions. Dis. dock. Econ. Sciences//SPb, Saint Petersburg state University-EF.
Patlasov O. Yu. (2014); Application of Altman models and criteria in the analysis of the financial state of agricultural enterprises]//Financial management. №6, 2006. [Electronic resource]//-Access mode; URL; http; //
Pegat A. Fuzzy modeling and control/A. Pegat; lane from English.-2nd ed. (el.).-M.; BINOM. Knowledge laboratory, 2013.-798 p.
Fomin, P. A. (2003); Features of accounting of financial risks at the forecast of dynamics of development of the economic entity. Finance and credit. No. 4.
Kharin Y. S., malugin V. I. and V. P. Cirilica, Lubach V. I., Khatskevich G. A. (1997); Fundamentals of simulation and statistical modeling. Meganewton.; Design PRO.
Shatalova A. Yu. Parametric α-level λ-continuation method for fuzzy linear programming problem//A. Yu. Shatalova, K. A. Lebedev/"Bulletin of the Buryat state University. Mathematics, Informatics",-№ 1, 2018.
Altman E. I., Iwanicz-Drozdowska M., Laitinen E. K., Arto Suvas Distressed Firm and Bankruptcy prediction in an international context; a review and empirical analysis of Altman’s Z-Score Model//9.07.2014.
Bamadio B., Lebedev K. A., Shevchenko I. V. (2016); Improvement of a five factor Altman model to assess the creditworthiness of an enterprise using the theory of fussy sets//Journal of Computations & Modelling, vol. 6, № 4.
Beaver W. (1967); Financial Ratio as Predictors of Failure, Empirical Research in Accounting//Journal of Accounting Research.-№ 4.
Fulmer J. (1984); A Bankruptcy Classification Model For Small Finns//Journal of Commercial Bank Lending. № 6.
Hiyama T., Sameshima T. (1991); Fuzzy logic control scheme for an-line stabilization of multi-machine power system//Fuzzy Sets and Systems. Vol. 39.
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