An algorithm for assessing the financial stability of Russian industrial enterprises based on Markov chains

Subject. The dynamics of changes in the financial condition of an enterprise, viewed through the prism of six discrete states – from stable to crisis. Objectives. Creation of an algorithm to increase the validity of management decisions. Solving problems of predicting probabilistic transitions between enterprise conditions, quantifying annual risks and determining the likelihood of achieving profitability targets. Methods. The work is based on the synthesis of methods of stochastic modeling, factor analysis and simulation analysis. The methodological framework includes the theory of Markov chains, the DuPont three-factor model, as well as the Monte Carlo method for uncertainty accounting. The developed algorithm implements the dual-circuit use of a random number generator, which allows for separate modeling of internal business processes and external market shocks. Results. The developed algorithm makes it possible to determine with high accuracy the probability of transitions between the states of the enterprise, calculate the mathematical expectation of annual risks and a given profitability. The results demonstrate that taking into account the multicomponent nature of profitability, combined with modeling its probabilistic dynamics, provides management with a more reliable basis for risk planning and management. Conclusions. The proposed approach forms a new methodological basis for strategic financial management in conditions of uncertainty. It is established that the integration of Markov chains, the DuPont model and the Monte Carlo method creates a synergetic effect, providing not only an in-depth analysis of the current state, but also predictive analytics. The results obtained open up prospects for the implementation of the developed algorithm in the practice of financial analysis and the construction of management decision support systems.