Mapping the Knowledge Landscape of Machine Learning in Portfolio Optimization: A Bibliometric Analysis of Asset Allocation Research

Subject Areas : Financial and Economic Modelling

Mahsa Safavi Iranji ¹ , Mojgan Safa ² , Majid Zanjirdar ³ , Hossein Jahangirnia ⁴

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Received: 2024-11-15 Accepted : 2025-03-14 Published : 2025-04-23

Keywords: Asset Allocation, Bibliometric Analysis , Machine Learning , Portfolio Optimization,

Abstract :

This study investigates the bibliometric analysis on asset allocation for portfolio optimization using machine learning algorithms. The primary objective is to identify and analyze the scientific literature through bibliometric analysis to uncover key themes, authors, sources, highly-cited articles, and countries involved in portfolio management research. To achieve this, 304 articles indexed in Scopus and Web of Science from 1990 to 2023 were analyzed. Using RStudio software, the study highlights various models employed in this field, along with tables, graphs, maps, and key performance metrics related to article production and citation impact. The findings reveal an upward trend in the use of machine learning for optimal portfolio management, asset allocation, and risk management since 2016. Additionally, the United States and China emerged as leading contributors to this literature. The results provide practical insights for market participants, especially those in fintech and finance sectors, to identify optimal machine learning solutions for decision-making processes. These findings also guide students in focusing their research efforts on underexplored areas within this domain.

References:

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