Work

Efficient Selection of Investment Portfolios in Real-World Markets: A Multi-Objective Optimization Approach

Year: 2025

Type: article

Abstract: As financial markets become increasingly complex, optimizing investment portfolios under multiple conflicting objectives has become a central challenge for decision-makers. This paper presents a comprehensive benchmarking framework for multi-objective portfolio optimization based on metaheuristics, designed to operate on real-world financial data. This framework integrates preprocessing, and optimization using four state-of-the-art algorithms: NSGA-II, MOEA/D, SMS-EMOA, and SMPSO. Using historical data from over 11,000 assets listed on U.S. exchanges, including ARCA, NYSE, NASDAQ, OTC, AMEX, and BATS, we define a suite of benchmark scenarios with increasing dimensionality and constraint complexity. Our results highlight algorithmic strengths and limitations, reveal significant trade-offs between return and risk, and demonstrate the effectiveness of multi-objective metaheuristics in constructing diversified, high-performance investment portfolios. Each portfolio is encoded as a real-valued vector combining asset selection and allocation, enabling fine-grained diversification control. All datasets and source code are publicly available to ensure reproducibility.

Source: Algorithms

DOI: 10.3390/a19010020

Authors: Antonio J. Hidalgo-Marín, Antonio J. Nebro, José García-Nieto

Institution: Universidad de Málaga

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funders: European Commission, Agencia Estatal de Investigación