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Assessing the Financial Stability of Electric Power Organizations

Author

Listed:
  • Olga V. Borisova

    (Financial University, Government of the Russian Federation, Moscow, Russia)

  • Olga A. Kalugina

    (Financial University, Government of the Russian Federation, Moscow, Russia)

  • Nikolay N. Kosarenko

    (Plekhanov Russian University of Economics, Moscow, Russia)

  • Aleksandr V. Grinenko

    (Moscow State Institute of International Relations (MGIMO University), Moscow, Russia,)

  • Izida I. Ishmuradova

    (Kazan (Volga region) Federal University, Kazan, Russia)

Abstract

Nowadays the economic processes in any world economy are carried out at a rapid pace. They have a strong influence on the activities of companies. In this regard, the electric power companies have faced the issue of increasing the efficiency of companies and reducing the degree of dependence on external factors. Moreover, the successful operation of the majority of modern branches of the national economy depends on the efficient and smooth performance of this business. The purpose of the study is to improve the financial stability indicators of electric power companies. The base of the conducted study includes both general scientific and empirical methods: analysis, synthesis, generalization, modeling, observation, description, measurement, and comparison and the case method, which allow broadening the authors understanding of the financial stability of the business, proposing its main criteria, and studying the aspects of the financial stability of business exemplified by electric power companies. The study carried out by the authors shows that the financial stability of the country is inextricably linked to the financial stability of organizations. Therefore, they need to be evaluated jointly. The relationship between macroeconomic indicators and the financial stability of business is defined by the authors. The concept of financial stability at the macro and micro levels is generalized. The indicators of financial diagnostics of organizations, characterizing their financial stability have been revealed. A model has been developed that allows determining the degree of the financial stability of electric power companies. The proposed model enables the selection of the most stable and steadily functioning electric power companies out of the significant number of ones in conditions of the uncertainty of the external environment. The results obtained by the authors will give an opportunity to identify not only the most stable business in the electric power industry but also, as a consequence, to determine the most attractive business model that should be adapted to other regions to minimize the adverse effects of situations related to uncertainty.

Suggested Citation

  • Olga V. Borisova & Olga A. Kalugina & Nikolay N. Kosarenko & Aleksandr V. Grinenko & Izida I. Ishmuradova, 2019. "Assessing the Financial Stability of Electric Power Organizations," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 66-76.
    • Handle: RePEc:eco:journ2:2019-03-8
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Irina A. Firsova & Dinara G. Vasbieva & Andrey V. Losyakov & Viktoriia S. Arhipova & Andrey A. Pavlushin, 2018. "Development of Active Consumer Concept on Energy Market," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 8-13.
    4. Philip Bunn & Victoria Redwood, 2003. "Company accounts based modelling of business failures and the implications for financial stability," Bank of England working papers 210, Bank of England.
    5. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    7. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2.
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    More about this item

    Keywords

    financial stability; indicators; financial conditions; business model; electric power companies.;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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