Application of Big Data Analytics in Financial Decision-Making: Integrating Computational Models to Optimize Investment Strategies

The application of big data analytics in financial decision-making has become pivotal in addressing the complexities of modern financial markets. With the growing availability of high-dimensional and high-frequency data, traditional investment strategies often fail to capture dynamic market behaviors and multi-scale dependencies. Conventional methods, grounded in static models and linear assumptions, lack the flexibility and robustness required for optimizing financial decision-making in volatile and interconnected markets. This paper aligns with the scope of computational advancements in financial systems, introducing an Adaptive Investment Optimization Model (AIOM) that integrates deep learning, stochastic modeling, and reinforcement learning to enhance investment strategies. By leveraging multi-scale feature extraction, dynamic market state estimation, and a reinforcement learning-based optimization engine, the model achieves superior adaptability and precision. Our novel Market-Aware Optimization Framework (MOF) further refines portfolio management by dynamically adjusting allocations based on predictive market signals and advanced risk measures, such as Conditional Value at Risk (CVaR) and drawdown control. Experimental results demonstrate significant improvements in portfolio returns and risk management compared to traditional methods. This work exemplifies the potential of computational innovations in transforming financial decision-making, offering robust solutions for real-time, adaptive investment optimization.