The article is devoted to the design of adaptive financial models for fast-growing technology companies that face a shortage of reliable forecasts due to sparse historical data. The relevance of this endeavor stems from the need to move beyond static forecasting toward dynamic valuation capable of responding to changing market conditions and structural business parameters. The novelty lies in the integration of simulation modeling, machine-learning techniques, Bayesian hyperparameter optimization, and streaming data analysis into a unified capital-management architecture. Within this framework, strategies for incorporating sentiment analysis and budget rebalancing are described, scenario-based valuation models are examined, and algorithms for calibrating PEG multiples according to growth phases are developed. Special attention is paid to validating expert assumptions through the analysis of customer metrics. The work establishes the goal of creating a modular structure able to adapt to phases of rapid, mature, and sustainable growth. To achieve this, comparative analysis, case studies, and cash-flow modeling are employed. Publications from CFO Drive, Forbes, IJEAT, Coherent Solutions, and Avenga are reviewed. The conclusion offers recommendations for implementing the framework in companies at various stages of development.

adaptive financial models, fast-growing companies, machine learning, Bayesian optimization, streaming data analysis, scenario analysis, PEG multiple, digital twins, budget rebalancing, forecasting

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