DYNAMIC MODELING OF CORPORATE FINANCIAL HEALTH: APPLYING METHODS FROM THE THEORY OF RANDOM PROCESSES

Аннотация

This article discusses dynamic modeling strategies that use the capabilities of the theory of random processes, offering a comprehensive framework for understanding and improving the financial condition of enterprises. Key words: Dynamic modeling, financial condition, stochastic processes, corporate finance, financial modeling, random processes, VUCA environment. In the constantly evolving world of corporate finance, financial condition management and active adaptation to it are essential. Rapid progress in technology has led to the creation of a world in which wired and wireless networks are currently undergoing undeniable changes, and smart spaces, the Internet, smartphones, and social networks are ubiquitous and easily accessible to billions of people around the world. Global events such as the COVID pandemic, the Azerbaijani-Armenian border crisis (since 2021), the US-Iranian conflict (since 2019), the conflict in South Kordofan (since 2011), Russia's invasion of Ukraine (since 2022), Turkey in Iraq (2022), Hamas in Israel (2023), the civil war in Myanmar (since 2021), in Sudan (2023), the feeling of turbulence, danger and unpredictability has increased. A sense of confidence, stability, and familiarity has replaced the state of constant change. In many industries, a growing wave of volatility, uncertainty, and complexity of business is shaking the markets and changing the nature of competition[1]. This type of environment can be described using the abbreviation "VUCA," which was introduced by the US Army War College [4], which is a cliche in the absence of a deeper analysis [3]. The components to which it refers - variability, uncertainty, complexity, and ambiguity - are words used differently to describe an environment that defies accurate diagnosis and confuses managers[2]. Given market fluctuations and the economic environment becoming increasingly unpredictable, a static approach to financial modeling may not effectively reflect the complex dynamics of a corporation's financial well- being. Traditional financial models rely on deterministic assumptions that assume a certain level of predictability and stability. However, the reality of modern business ecosystems is characterized by constant changes, which are influenced by many internal and external factors. To eliminate this complexity, our research focuses on applying methods based on the theory of random processes for dynamic modeling of the financial condition of a corporation. The main objective is to present a detailed look at integrating randomness and uncertainty into financial modeling, recognizing the inherently stochastic nature of financial markets and corporate transactions. Retail managers are facing the challenge of accurately forecasting changes in key performance indicators due to the stochastic uncertainty and increasing complexity of logistics processes. The authors developed a simulation model to assess the impact of risk events and unstable conditions on the quality of supply chain services and economic indicators of the retail chain. Using the whip effect, the simulation model enables managers to evaluate operational 163 risks and their impact on crucial supply chain indicators [5].Sample Heading (Forth Level). The contribution should contain no more than four levels of headings. The following Table 1 gives a summary of all heading levels. Table 1 Review of Traditional Approaches and Emerging Trends in Dynamic Financial Modeling. Traditional Description: Applicability Approaches Discounted Cash Evaluation of the asset or Widely used for investment Flows (DCF) project value based on valuation and financial decision- discounted future cash flows. making. Capital Asset Pricing Use of statistical methods to Identifies trends, cycles, and Model (CAPM) analyze and forecast time seasonal variations in financial series of financial data. data. Time Series Analysis A stochastic process where the Modeling systems with future state depends only on memoryless transitions, such as the current state and not on queueing systems. the sequence of events that preceded it Emerging Trends Description Applicability Stochastic Modeling Utilization of stochastic Enhances model accuracy by processes to account for considering unpredictable random factors in financial changes in the market models Machine Learning Application of machine Processes large volumes of data (ML) in Finance learning algorithms for data and identifies complex analysis and trend prediction dependencies. in financial markets. Integration of Fusion of stochastic modeling Improves model adaptability to Random Processes methods and random market dynamics. Theory processes theory for more accurate reflection of random changes in financial environments. Assessment of Assessment Outcome Effectiveness and Applicability Effectiveness Comparison of forecast Determines how new methods Assessment (MA) accuracy between traditional can enhance forecasting and Process and new approaches using