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Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features

Author

Listed:
  • E. Fedorova A.

    (Financial University)

  • M. Chukhlantseva A.

    (National Research University Higher School of Economics)

  • D. Chekrizov V.

    (“Globalstar-Space Telecommunications” Joint Stock Company)

  • ЕЛЕНА Федорова АНАТОЛЬЕВНА

    (Финансовый университет)

  • МАРИЯ Чухланцева АЛЕКСАНДРОВНА

    (Национальный исследовательский университет «Высшая школа экономики»)

  • ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ

    (Акционерное общество «Глобалстар-Космические Телекоммуникации»)

Abstract

The purpose of this study is to predict the Russian companies’ bankruptcy probability based on existing legislation. The empirical base of the study consists of the collection of financial statements of 2017 enterprises (866 of them gone bankrupt) belonging to four economic sectors: wholesale trade, construction, power generation, food production. In the course of investigation the authors have examined the consistency of current normative values of financial ratios approved by the regulatory acts of the Russian Federation, as well as proposed their redefined data based on economic and mathematic modeling. The worked out norms of financial stability allow classifying companies with sufficient accuracy as bankrupts and financially healthy companies (from 75% to 85%). The given norms have been calculated for two groups of insolvent companies: 1) formally declared bankrupts; 2) officially declared bankrupts and the companies, which are the stage of the arbitration proceedings according to creditors’ claims. The obtained results can be applied in enterprises’ crisis management decision-making. Целью данной работы является прогнозирование вероятности банкротства российских компаний на основе действующего законодательства. Эмпирическая база включает в себя 2017 компаний (из них 866 - банкроты) по четырем секторам экономики: оптовая торговля, строительство, производство электроэнергии, а также производство пищевых продуктов. В ходе исследования авторами была проверена состоятельность текущих нормативных значений финансовых коэффициентов, утвержденных нормативными актами Российской Федерации, а также предложены их уточненные значения на основе экономико-математического моделирования. Разработанные нормативы финансовой устойчивости позволяют классифицировать предприятия-банкроты и здоровые организации с точностью 75-85%. Данные нормативы рассчитаны для двух групп несостоятельных компаний: 1) официально признанные банкроты; 2) официально признанные банкроты и предприятия, которые проходят стадии арбитражного производства по исковым заявлениям кредиторов. Полученные результаты могут использоваться при принятии управленческих решений по антикризисному управлению предприятий.

Suggested Citation

  • E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.
    • Handle: RePEc:scn:mngsci:y:2017:i:2:p:44-55
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    References listed on IDEAS

    as
    1. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    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.
    3. Keasey, Kevin & Watson, Robert, 1989. "Consensus and accuracy in accounting studies of decision-making: A note on a new measure of consensus," Accounting, Organizations and Society, Elsevier, vol. 14(4), pages 337-345, July.
    4. 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.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    6. Rowoldt, Maximilian & Starke, Dennis, 2016. "The role of governments in hostile takeovers – Evidence from regulation, anti-takeover provisions and government interventions," International Review of Law and Economics, Elsevier, vol. 47(C), pages 1-15.
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