Stochastic Modeling of a Company’s Financial Condition under Uncertainty

Анотація

The aim of this article is to develop a general algorithm for modeling a company’s financial condition using a stochastic approach, taking into account the high-level uncertainty in which companies operate. A wide range of methods and models is used for modeling (analyzing, forecasting, evaluating) the financial condition: economic and econometric-mathematical – both classical and innovative. Stochastic modeling represents an innovative direction. The article substantiates the feasibility of applying a probabilistic approach in financial analysis under conditions of uncertainty and risk, particularly for modeling a company’s financial condition. The main stochastic modeling methods used in modern financial analysis include: Monte Carlo method, Markov processes, Brownian models, stochastic differential equations, Bayesian networks, GERT networks, regression modeling with random parameters, and others. A brief overview of these methods is provided: their essence, advantages, disadvantages, and usage examples. A general algorithm for constructing a stochastic model of a company’s financial condition has been developed. The main steps are: 1) defining the modeling objectives; 2) identifying key financial indicators; 3) choosing the type of stochastic model; 4) collecting and processing input data; 5) building and calibrating the model; 6) verifying, testing, and validating the model; 7) interpreting the modeling results; 8) performing sensitivity analysis and stress testing; 9) documenting and updating the model. Emphasis is placed on the consideration that stochastic modeling of a company’s financial condition has both advantages and limitations (restrictions of application). Despite some limitations, stochastic modeling is a powerful tool for analyzing a company’s financial condition, but its efficiency depends on the appropriate choice of methods, the quality of input data, and the level of expertise of financial analysts.

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