Year: 2025
Type: article
Study design: Systematic Review
Abstract: ABSTRACT This systematic review identifies persistent challenges and gaps in the literature on pension portfolio optimization models. We searched, selected, and critically analyzed 82 articles from three major academic databases published over the past decade to investigate the barriers to the effective implementation of these models. Most studies primarily focus on traditional portfolio optimization approaches, often overlooking recent innovations and the unique structural characteristics of pension systems. While the number of publications has grown steadily over the past 10 years, reflecting increasing academic interest in the topic, our analysis reveals a continued need for substantive research advances to bridge existing gaps. Key challenges include the lack of models tailored to the specific features of pension funds, limited attention to longevity and sustainability constraints, and insufficient application of innovative methods to address sector‐specific demands. This study highlights the importance of future research that incorporates advanced machine learning techniques, develops ESG‐aligned and sustainability‐oriented optimization frameworks, and more effectively models regulatory and demographic constraints. These directions are essential for enhancing the robustness and relevance of pension portfolio models and to support more effective, long‐term pension fund management.
Source: Journal of Economic Surveys
DOI: 10.1111/joes.12702
Authors: Leonardo Moreira, Igor Leão dos Santos, Pedro Henrique Gonzalez
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,250
OpenAlex ID: W4410133223