Mapping portfolio optimisation: a systematic and bibliometric review - Agrawal, Preeti Bai; Anuradha Samal , Dr. (Global Journal of Mathematical Analysis, 2024)
Purpose: This study provides a comprehensive analysis of the evolution of portfolio optimization over the last three decades, employing systematic review and advanced bibliometric techniques to map key trends, influential works, and significant contributors in the field.Design/Methodology/Approach: Adhering to PRISMA guidelines, we conducted a systematic review and bibliometric analysis of 1,000 articles sourced from the Web of Science database, spanning from 1989 to 2023. Advanced bibliometric tools, including citation analysis, co-occurrence analysis, and network visualization, were utilized to identify prominent authors, influential journals, and emerging research themes.Findings: Our analysis reveals a significant growth in portfolio optimization literature, particularly in recent years. Key findings include the identification of pivotal authors, foundational papers, and leading journals that have shaped the field. The study also traces the methodological evolution from traditional models, like Markowitz's Modern Portfolio Theory, to contemporary approaches incorporating artificial intelligence and machine learning.Practical Implications: This study offers valuable insights for researchers and practitioners by highlighting critical developments in portfolio optimization. It also suggests areas for future research, particularly in integrating advanced data analytics and AI-driven methodologies into portfolio management.Originality/Value: This paper stands out by combining systematic review with a comprehensive bibliometric analysis, offering a holistic view of the portfolio optimization landscape. It not only synthesizes past research but also identifies emerging trends and gaps, providing a foundation for future explorations in this dynamic field.