Non-Linear Data Representation with Machine Learning for Dynamic Covariance Based Financial Portfolio Optimization

This study addresses critical gaps in financial risk assessment and portfolio optimization by integrating advanced machine learning (ML) and deep learning (DL) techniques to handle the complexities of non-linearity, non-normality, and dynamic correlations among financial assets. This study comprehensively analyzes various dimensionality reduction techniques across different financial assets and time periods. By extracting non-linear features and constructing dynamic, data-driven covariance matrices, both linear and non-linear interactions among assets have been captured. This novel methodology hybridizes ML/DL and statistical approaches to enhance the robustness and resilience of portfolio optimization. The findings demonstrate significant improvements in profitability and stability under varying market conditions, offering a substantial advancement over prior studies. Therefore, this research provides a pioneering framework for more accurate and dynamic financial analysis, setting a novel standard in this research direction.