DEVELOPMENT OF ADAPTIVE FINANCIAL STRATEGIES BASED ON SCENARIO ANALYSIS AND MACHINE LEARNING IN DYNAMICALLY CHANGING MARKETS

Apstrakt

The article examines adaptive financial strategies in volatile markets, focusing on the timing of managerial response and decision latency in financial decision-making. The role of scenario analysis as a tool for structuring uncertainty and assessing the resilience of financial indicators under alternative macroeconomic and industry trajectories is analyzed. The importance of machine learning methods for improving forecasting accuracy, identifying nonlinear relationships, and dynamically updating model parameters is emphasized. It is concluded that the integration of scenario analysis and machine learning forms a predictive financial control framework that enhances cash flow stability, optimizes capital structure, and strengthens the long-term competitiveness of companies. The study further introduces a temporal decision-making dimension by incorporating early operational signal detection, reducing decision latency between economic events and managerial response.

JEL: G32, C53, G31