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.

Contemporary information technology applications are overwhelmed by big data and require advanced data science analytics for careful investigation, interpretation, and predictions. Data sets in various applications exhibit high frequency with non-linear dynamic variability, and hence, leveraging the strengths of data-driven feature selection and sophisticated machine learning architectures becomes essential.This study proposes two novel architectures- Data-Driven Long Short-Term Memory (DD-LSTM) and Data-Driven Gated Recurrent Unit (DD-GRU), to improve predictive accuracy for highly fluctuating time-series data. Input data are log-transformed and used to derive data-driven risk forecasts and non-linear residuals based on underlying statistical features, which are then integrated with normalized original data into hyperparameter-optimized LSTM and GRU models. Experimental results with a financial dataset show that the proposed frameworks significantly outperform conventional LSTM and GRU by capturing intricate temporal patterns and risk dynamics. DD-GRU, in particular, exhibits greater computational efficiency, making it a robust solution for modeling nonlinear and irregular time-series data. This research not only addresses the critical challenge of optimizing temporal feature selection in high-frequency datasets but also offers a robust framework for analyzing complex temporal patterns across diverse high-frequency data sources.

Forecasting stock market indices is a challenging task due to the inherent complexity, non-linearity, and stochastic nature of financial time series. Although deep learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, outperform traditional econometric methods in capturing temporal dependencies, their standalone implementations often lack robustness and generalizability. This study presents a novel hybrid framework combining recurrent neural networks with Support Vector Regression (SVR) to address these limitations. The proposed approach integrates the sequential learning capabilities of LSTM and GRU with the nonlinear regression strengths of SVR, achieving superior predictive performance across diverse global indices. Empirical results highlight significant improvements over baseline models in both accuracy and adaptability to varying market conditions. The hybrid framework demonstrates its effectiveness in merging the advantages of its components, providing robust, generalizable predictions with reduced susceptibility to overfitting. These findings pave the way for future research in hybrid financial forecasting methods and their practical applications.

Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.