Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques

This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel at capturing long-term dependencies in financial data, leading to more robust predictions and improved portfolio outcomes. Feature selection and preprocessing were crucial in maximizing model performance. Portfolio optimization using machine learning models, when combined with traditional optimization techniques, resulted in superior Sharpe and Sortino ratios. These findings highlight the potential of machine learning to enhance real-time financial decision-making, offering more adaptive and resilient strategies for managing investment portfolios in dynamic market environments. This research provides valuable insights into the integration of machine learning for financial risk prediction and portfolio management, with implications for future advancements in the field.