Adaptive budgeting and cost management of agricultural enterprises based on risk-oriented indicators

ntroduction. The modern agricultural sector in Ukraine operates under conditions of high market volatility, structural financial changes, and seasonal cash flow fluctuations, creating significant challenges for ensuring enterprise financial stability. Integrating effective strategic financial planning and cost management methods is therefore highly relevant. Problem Statement. Existing strategic financial planning approaches often fail to account for risk-oriented indicators and timely identification of cash gaps, increasing the likelihood of liquidity deficits and credit burden. The need for higher forecasting accuracy and adaptability of financial strategies to changing market conditions emphasizes the importance of this problem. The purpose is to develop a methodology for integrating risk-based indicators into the strategic financial planning process of agricultural enterprises while simultaneously improving cost management, which ensures increased financial stability and efficiency of credit management. Methods. The research is grounded in system and dialectical approaches, enabling comprehensive analysis of interrelations between credit load, cash gaps, and cost management. Quantitative assessments employed econometric and statistical models, parametric and non-parametric risk assessment methods, and modern machine learning algorithms (XGBoost) within an ensemble Value-at-Risk framework. Results. The proposed methodology improves liquidity deficit forecasting, optimizes credit portfolio structure, and allows timely detection of cash shortfalls. The Liquidity Gap indicator as a key solvency assessment tool confirms the effectiveness of the approach. Analysis of the results demonstrates the feasibility of integrating risk-oriented indicators into strategic financial planning and cost management. Conclusions. The findings have theoretical significance for expanding the methodological toolkit of financial strategizing and practical significance for enhancing the financial stability of agricultural enterprises, optimizing credit load, and strengthening market competitiveness. Future research prospects include extending ensemble risk models to incorporate macroeconomic and climate factors and developing digital platforms to automate strategic financial planning.