Affiliation:
1. Lomonosov Moscow State University
Abstract
The paper provides the solution to the problem of an integrated classification of existing bankruptcy prediction based on the content analysis of 270 relevant foreign and Russian publications issued within a period of 1910-2020. The author identifies two main groups of models– normative and positive, with the latter categorized into expert, mixed and objective including traditional statistical models and artificial intelligent techniques; and considers the specific features of certain predicting models, their advantages and disadvantages. He then reveals the economic content of such models and the set of ratios applied for identifying company’s financial distress with the following conclusions: approaches to the variables selection are rarely justified, indicators are usually borrowed from previous models or generated automatically by the database configuration; the accounting approach to bankruptcy forecasting based on financial ratios prevails and has serious limitations for Russian companies; the most significant market, value and qualitative variables indicating a decline in the business financial stability are highlighted. Significant limitations of the general use of bankruptcy prediction models for making decisions aimed at insolvency prevention are identified: the inability to anticipate the impact of informal factors that are irregular, unable to extrapolate, and affect companies in different ways; the need to take into account the economic conditions of the national economy, financial reporting standards, and the level of availability of diverse data; the impossibility of creating a universal indicative basis to identify decline of sustainability of any business due to the high volatility of operating conditions in Russia. Bayesian methods and nowcasting, as well as the development of forecasting models for certain industries, are promising areas for the development of modern approaches to bankruptcy prediction, but the fundamental activity for preventing insolvency is not forecasting by models, but the implementation of continuous monitoring of the overall business performance in relation to influencing market, operational, investment, financial, managerial and organizational factors, taking into account significant qualitative variables.
Publisher
Faculty of Economics, Lomonosov Moscow State University
Subject
Electrical and Electronic Engineering,Building and Construction
Reference53 articles.
1. Ариничев, И. В., Матвеева, Л. Г., & Ариничева, И. В. (2018). Прогнозирование банкротства организации на основе метрических методов интеллектуального анализа данных. Вопросы регулирования экономики, 9(1), 62–73. https://doi.org/10.17835/2078-5429.2018.9.1.061-073
2. Бобылева, А. З., & Львова, О. А. (2019a). Управление трансформационными программами слияний и присоединений с участием проблемных компаний. Вестник Санкт-Петербургского университета. Менеджмент, 18(4), 483–509. https://doi.org/10.21638/11701/spbu08.2019.401
3. Бобылева А. З., & Львова О. А. (2019b). Предупреждение банкротства: институциональная поддержка слияний и присоединений проблемных компаний. Проблемы теории и практики управления, 7, 100–112.
4. Горбатков, С. А., Белолипцев, И. И., & Макеева Е. Ю. (2013). Выбор системы экономических показателей для диагностики и прогнозирования банкротств на основе нейросетевого байесовского подхода. Финансы: теория и практика, 4, 50–61.
5. Макеева, Е. Ю., Аршавский, И. В. (2014). Применение нейронных сетей и семантического анализа для прогнозирования банкротства. Корпоративные финансы, 8(4), 130–140.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献