Corporate bankruptcy and crises: analysis and trends

Devyatkin O.V.¹
¹ Российский экономический университет им. Г.В. Плеханова, Russia

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Journal paper

Journal of Economics, Entrepreneurship and Law ^{(РИНЦ, ВАК)}
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Volume 13, Number 7 (July 2023)

Citation:

Indexed in Russian Science Citation Index: https://elibrary.ru/item.asp?id=54310344
Cited: 1 by 07.12.2023

Abstract:
The author analyzes the official statistics on the bankruptcy of organizations for 2022 and the Q1 of 2023 and draws conclusions about the trends in the development of the crisis situation in 2023. The analysis of the main indicators allows to conclude that the number of bankruptcies in the current 2023 is likely to increase. It is noted that there is a disproportionately large number of bankruptcy proceedings in comparison with the number of procedures aimed at restoring solvency, financial recovery and rehabilitation of debtors. As a result, creditors do not receive reimbursement of their claims, which is assessed as a direct loss. In addition, economic ties and supply chains are disrupted, which negatively affects the economy as a whole. It is proposed to discuss at the legislative level the possibility of conducting a mandatory financial recovery procedure for certain categories of debtors. Consideration of data on the total number of conclusions on the presence of signs of deliberate and fictitious bankruptcy is small, which indicates a real crisis state of enterprises and organizations. To prevent bankruptcies as an extreme manifestation of the crisis, it is proposed to apply a conceptually new innovative direction for ensuring strategic sustainable development. It is the mechanism of autogenic self-generated. In other words, controlled crisis.

Keywords: crisis, crisis situations, bankruptcy, turnaround management, anticrisis management, autogenic crisis

JEL-classification: G32, G33, G01

References:

Ekonomicheskie krizisy [Economic crises] (0). (in Russian).

Altman E. I., Haldeman R. G., Narayanan P. (1977). Zeta analysis: a new model to identify bankruptcy risk of corporation Journal of Banking and Finance. (1). 29–54.

Altman Edward I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy Journal of Finance. 189—209. doi: 10.1111/j.1540-6261.1968.tb00843.x.

Barnett Carole K., Pratt Michael G. (2000). From threat‐rigidity to flexibility ‐ Toward a learning model of autogenic crisis in organizations Journal of Organizational Change Management. (13(1)). 74-88. doi: 10.1108/09534810010310258.

Beaver W. (1966). Financial Ratios as Predictors of Failure Journal of Accounting Research. (4). 71–111. doi: 10.2307/2490171.

Berezinets I. V., Bobyleva A. Z., Ilyina Yu. B. (2022). Veroyatnost bankrotstva: dostoverny li modeli prognozirovaniya? [Probability of bankruptcy: are the forecasting models reliable?]. Public administration. Electronic Bulletin. (94). 68-83. (in Russian). doi: 10.24412/2070-1381-2022-94-68-83.

Bobyleva, A. Z., Lvova O. A. (2020). Finansovo-ekonomicheskiy instrumentariy vyyavleniya priznakov obektivnogo bankrotstva [Financial-economic tools for identifying the signs of objective bankruptcy]. Current problems of economics and law. (1). 22–39. (in Russian). doi: 10.21202/1993-047X.14.2020.1.22-39.

Devyatkin O. V. (2019). Autogennyy krizis kak kontseptualnaya model sistemnyh izmeneniy [Autogenous crisis as a conceptual model of system changes]. Vestnik REU imeni G. V. Plekhanova. (2 (104)). 182-191. (in Russian). doi: 10.21686/2413-2829-2019-2.

Devyatkin O. V. (2019). Stress-testirovanie v sisteme autogennogo krizisa [Stress testing as a crisis management tool]. Scientific research and development. Economy of the company. (4). 73-83. (in Russian). doi: 10.12737/2306-627X-2019-71-81.

Devyatkin O.V. (2023). Stress-testirovanie kak instrument upravleniya krizisnymi situatsiyami v organizatsiyakh [Stress testing as a crisis management tool]. Leadership and Management. 10 (1). 81-92. (in Russian). doi: 10.18334/lim.10.1.117422.

Fedorova E. A., Dovzhenko S. E., Fedorov F. Yu. (2016). Modeli prognozirovaniya bankrotstva rossiyskikh predpriyatiy: otraslevye osobennosti [Bankruptcy-prediction models for Russian enterprises: specific sector-related characteristics]. Problems of forecasting. (3). 32-40. (in Russian).

Garcia J. (2022). Bankruptcy Prediction Using Synthetic Sampling Machine Learning with Applications. (9). doi: 10.1016/j.mlwa.2022.100343.

Jardin P. (2021). Dynamic Self-Organizing Feature Map-Based Models Applied to Bankruptcy Prediction Decision Support Systems. (147). doi: 10.1016/j.dss.2021.113576.

Lvova O. A., Peganova O. M. (2014). Faktory i prichiny bankrotstva kompaniy v usloviyakh sovremennoy ekonomiki [Factors and reasons of companies bankruptcy in present economic conditions]. Public administration. Electronic Bulletin. (44). 64-82. (in Russian).

Qu Y., Quan P., Lei M., Shi Y. (2019). Review of Bankruptcy Prediction Using Machine Learning and Deep Learning Techniques Procedia Computer Science. (162). 895–899. doi: 10.1016/j.procs.2019.12.065.

Robert Drazin, Lloyd Sandelands (1992). Autogenesis: A Perspective on the Process of Organizing Organization Science. (3(2)). 230-249. doi: 10.1287/orsc.3.2.23.

Shumway T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model Journal of Business. (74(1)). 101–124. doi: 10.1086/209665.

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