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Improving the Altman Method for Assess the Creditworthiness of Enterprises with Economic Indicators in the Form of Fuzzy Numbers

Received: 13 August 2019     Accepted: 6 September 2019     Published: 28 May 2020
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Abstract

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.

Published in Engineering Mathematics (Volume 4, Issue 1)
DOI 10.11648/j.engmath.20200401.12
Page(s) 10-13
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Assessment of the Creditworthiness, Altman Model, Fuzzy Sets, Membership Function, Simulation, Decision-making Under Uncertainty, Errors in the Financial Statements

References
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[14] 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; //dis.ru/library/699/26221/.
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[16] 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.
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[18] 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.
[19] 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.
[20] 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.
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  • APA Style

    Alevtina Shatalova, Konstantin Lebedev, Igor Shevchenko, Boureima Bamadio. (2020). Improving the Altman Method for Assess the Creditworthiness of Enterprises with Economic Indicators in the Form of Fuzzy Numbers. Engineering Mathematics, 4(1), 10-13. https://doi.org/10.11648/j.engmath.20200401.12

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    ACS Style

    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. Eng. Math. 2020, 4(1), 10-13. doi: 10.11648/j.engmath.20200401.12

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    AMA Style

    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. Eng Math. 2020;4(1):10-13. doi: 10.11648/j.engmath.20200401.12

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  • @article{10.11648/j.engmath.20200401.12,
      author = {Alevtina Shatalova and Konstantin Lebedev and Igor Shevchenko and Boureima Bamadio},
      title = {Improving the Altman Method for Assess the Creditworthiness of Enterprises with Economic Indicators in the Form of Fuzzy Numbers},
      journal = {Engineering Mathematics},
      volume = {4},
      number = {1},
      pages = {10-13},
      doi = {10.11648/j.engmath.20200401.12},
      url = {https://doi.org/10.11648/j.engmath.20200401.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.engmath.20200401.12},
      abstract = {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.},
     year = {2020}
    }
    

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    AU  - Alevtina Shatalova
    AU  - Konstantin Lebedev
    AU  - Igor Shevchenko
    AU  - Boureima Bamadio
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    N1  - https://doi.org/10.11648/j.engmath.20200401.12
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    UR  - https://doi.org/10.11648/j.engmath.20200401.12
    AB  - 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.
    VL  - 4
    IS  - 1
    ER  - 

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Author Information
  • Department of Applied Mathematics, Kuban State University, Krasnodar, Russia

  • Department of Applied Mathematics, Kuban State University, Krasnodar, Russia

  • Faculty of Economics, Кuban State University, Krasnodar, Russia

  • Faculty of Economics and Management, University of Social Sciences and Management of Bamako, Mali, Bamako

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