Paper Contents
Abstract
The integration of data science into financial portfolio management has introduced transformative capabilities that significantly enhance decision-making, risk assessment, and return maximization. This paper presents an in-depth analysis of how modern data science techniques such as machine learning, big data analytics, and predictive modeling can be leveraged to optimize financial portfolios. Emphasizing dynamic market adaptation, risk management, and return optimization, the paper proposes a comprehensive framework that blends traditional financial theory with advanced algorithms. Through case studies, the discussion illustrates the profound impact of real-time data analytics and sentiment analysis in optimizing asset allocation and rebalancing strategies.INTRODUCTION Portfolio management involves the art and science of making decisions about investment mix and policy, matching investments to objectives, and balancing risk against performance. Traditionally, portfolio optimization methods such as Markowitzs mean-variance model focus on maximizing return for a given level of risk. However, these models often struggle to account for complex, real-time market dynamics. With the rise of data science, financial portfolio management can now take advantage of more granular, comprehensive, and dynamic decision-making tools.
Copyright
Copyright © 2025 KHANSA IQBAL KHAN. This is an open access article distributed under the Creative Commons Attribution License.
