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Hybrid Learning-Based Dynamic Optimization for Financial Risk Management: Integrating Nonlinear Dynamics and Deep Learning
Abstract
Amid the increasing complexity and dynamic nature of financial markets, accurately capturing market fluctuations and implementing effective risk monitoring remain critical challenges in financial regulation. Traditional differential equation models, while proficient in theoretical derivation and variable representation, face significant limitations in handling high-dimensional, complex data and nonlinear characteristics. Conversely, deep learning technologies, with their robust feature extraction and time-series modeling capabilities, present transformative opportunities for financial data analysis. However, the trade-off between high-precision modeling and interpretability creates notable challenges for single-method approaches. To address these limitations, this study proposes a dynamic optimization framework, CT-BCIR, which integrates traditional differential equations with deep learning methodologies. The framework employs convolutional neural networks (CNNs) to extract local temporal features and long short-term memory networks (LSTMs) to capture long-range dependencies.
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