Strategic Decision Making in Uncertain Environments Using Optimized Financial Model

Abstract

Strategic decision-making in uncertain environments requires robust financial models to analyze historical data, predict trends, and mitigate risks. This study evaluates the effectiveness of machine learning-driven financial models, specifically Random Forest and Long Short-Term Memory (LSTM) networks, in forecasting stock price movements. The dataset, encompassing historical financial data such as closing prices, adjusted close prices, and trading volumes, serves as the foundation for predictive modeling. The research employs Monte Carlo simulations, scenario analysis, and stress testing to assess financial risk and improve decision-making resilience. Model evaluation metrics, LSTM excels in long-term trend analysis by capturing sequential dependencies in financial time series data. Findings indicate that AI-driven financial modeling enhances forecasting accuracy, optimizes investment strategies, and enables data-driven decision-making. The integration of scenario-based risk assessment and real-time predictive analytics supports financial resilience in volatile markets. This study contributes to financial analytics by providing a comparative analysis of traditional and deep learning-based models, offering practical insights for investors, financial analysts, and policymakers.