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DOI: Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques
Aftab Uddin , Fox School of Business & Management, Temple University, USA Md Amran Hossen Pabel , Master’s of Science in Business Analytics Wright State University, Ohio, USA Md Imdadul Alam , Master of Science in Financial Analysis, Fox School of Business, Temple University, USA FNU KAMRUZZAMAN , Department of Information Technology Project Management & Business Analytics, St. Francis College, USA Md Sayem Ul Haque , MBA in Business Analytics, Gannon University, USA Md Monir Hosen , Master of Business Administration in Supply Chain Management, University of Houston downtown, USA Ashadujjaman Sajal , Department of Management Science and Quantitative Methods, Gannon University, USA Mohammad Rasel Miah , MBA in Accounting, University of the Potomac, Leesburge pike, Falls church, Virginia, USA Sandip Kumar Ghosh , Department of Business Administration, University of Surrey, Guildford, Surrey, GU2 7XH, UKAbstract
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.
Keywords
Machine learning, financial risk prediction, portfolio optimization, asset returns forecasting
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Copyright (c) 2025 Aftab Uddin, Md Amran Hossen Pabel, Md Imdadul Alam, FNU KAMRUZZAMAN, Md Sayem Ul Haque, Md Monir Hosen, Ashadujjaman Sajal, Mohammad Rasel Miah, Sandip Kumar Ghosh
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