ecrСтратегические решения и риск-менеджментStrategic decisions and risk management2618-947X2618-9984Real Economy Publishing House10.17747/2078-8886-2018-1-64-71ecr-753Research ArticleСтатьиОтраслевые особенности применения моделей прогнозирования банкротства предприятияIndustry characteristic of bankruptcy prediction models applianceФедороваЕ. А.FedorovaE. A.

Д.э.н., профессор департамента корпоративных финансов и корпоративного управления ФГОБУ ВО «Финансовый университет при Правительстве Российской Федерации». Область научных интересов: экономико-математические методы и модели, финансовый менеджмент, прогнозирование банкротства.

Doctor of Economics, Professor of the Department of Corporate Finance and Corporate Management at the FGOBU VO “Financial University under the Government of the Russian Federation”. Research interests: economic and mathematical methods and models, financial management, bankruptcy forecasting.

ecolena@mail.ruХрустоваЛ. Е.KhrustovaL. E.

Аспирант департамента корпоративных финансов и корпоративного управления ФГОБУ ВО «Финансовый университет при Правительстве Российской Федерации». Область научных интересов: прогнозирование банкротства, финансовый контроль, финансовый менеджмент.

Postgraduate student of the Department of Corporate Finance and Corporate Management at the FGOBU VO “Financial University under the Government of the Russian Federation”. Research interests: bankruptcy forecasting, financial control, financial management.

khrustoval@yandex.ruЧекризовД. В.ChekrizovD. V.

Главный финансовый аналитик АО «ГлобалТел». Область научных интересов: финансовый менеджмент, прогнозирование банкротства.

Chief financial analyst of JSC “GlobalTel”. Research interests: financial management, bankruptcy forecasting.

chekrizovdv@mail.ruФГОБУ ВО «Финансовый университет при Правительстве Российской Федерации»РоссияFGOBU VO “Financial University under the Government of the Russian Federation”Russian FederationАО «ГлобалТел»РоссияJSC “GlobalTel”Russian Federation201824052018016471Copyright © Федорова Е.А., Хрустова Л.Е., Чекризов Д.В., 20182018Федорова Е.А., Хрустова Л.Е., Чекризов Д.В.Fedorova E.A., Khrustova L.E., Chekrizov D.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.jsdrm.ru/jour/article/view/753

Исследование предпринято для совершенствования методологии прогнозирования банкротства путем уточнения нормативных значений существующих моделей с учетом отраслевой принадлежности компаний и для разработки авторской модели прогнозирования банкротства. Прежде всего, оценена точность прогноза для компаний 8 отраслей по действующим нормативам моделей прогнозирования банкротства. Применение методологии CART (Classification And Regression Tree) позволило уточнить оригинальные нормативные значения и предложить новые индивидуальные границы оценки для каждой отрасли. Рассчитанные значения продемонстрировали высокую прогностическую способность и позволили сбалансировать показатели точности прогнозирования для компаний-банкротов и финансово устойчивых организаций. Из общей совокупности финансовых показателей, используемых в различных моделях, были отобраны коэффициенты, обладающие максимальной значимостью для прогнозирования банкротства. На их основе была разработана новая модель, демонстрирующая высокую точность результатов на заданной выборке, и нормативы ее оценки для компаний различных отраслей. Практическое применение предлагаемых разработок позволит повысить эффективность и достоверность прогнозирования банкротства, позволит своевременно скорректировать финансовое состояние компаний, которым грозит банкротство.

The aim of the research is to develop the methodology of bankruptcy prediction applying the specified statutory values of the existing models with a glance to company’s industry and developing the author’s prediction model. Initially authors estimated the forecast accuracy of the existing models for the enterprises of 8 industries. Using CART (Classification And Regression Tree) methodology the original statutory values of the models were specified for every industry under research. The calculated statutory values demonstrated the high level of prediction accuracy and balanced the indicators of accuracy for bankrupt and non-bankrupt companies. The indicators with the maximum level of significance for bankruptcy prediction were selected from all the models. They formed a basis for a new developed model, which has demonstrated the high level of prediction accuracy on a sample under research. The statutory values for the new model were also developed.The implementation of the research’s results will increase the efficiency of bankruptcy prediction and low the number of bankrupt companies.

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The authors declare that there are no conflicts of interest present.