The deterioration of the financial situation of enterprises in various industries, which is currently observed in many regions, creates prospects for reducing their financial stability and the emergence of bankruptcy risks. It is necessary to develop existing methodological approaches so as to assess and forecast the risks of bankruptcy for industries as a whole, rather than for individual enterprises, and form effective state support mechanisms for them. The main goal of our research is to develop these approaches. The novelty of the presented approach consists in the following facts: we develop an algorithm for scenario modeling and forecasting the risks of bankruptcy for industries, including an assessment of the financial stability of the industries under consideration and the probability of bankruptcy; we design regression models showing the dependence of change in the probability of bankruptcy on a whole system of internal and external factors; we conduct autoregressive modeling of the dynamics of internal and external factors using a moving average (ARMA). Autoregressive modeling allows us to form the most probable, inertial forecast scenario for the next five years taking into account the preservation of the noted trends and the corridor of maximum possible values. The forecast values of the dynamics of these factors and the constructed regression models serve as a basis for designing forecast scenarios for changes in the probability of bankruptcy of large, medium and small machine-building enterprises in the Sverdlovsk Region. As a result, we reveal that major machine-building holdings of the Sverdlovsk Region that do not have sufficient working capital, have a low level of solvency, and experience a significant debt burden are most susceptible to bankruptcy risks. In order to increase financial stability and effective development of large machine-building enterprises, government support is required
Keywords
financial stability, industries, scenario forecasting, bankruptcy risks, autoregressive modeling