Hybrid Metaheuristic Design for Solving the Cardinality-Constrained Portfolio Problem: Comparative Analysis in Six Equity Markets

Authors

  • Mohammad Hossain Noripour * Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.

https://doi.org/10.48314/anowa.v2i1.69

Abstract

We present a hybrid metaheuristic for mean–variance portfolio optimization with a cardinality constraint. The algorithm combines Continuous Ant Colony Optimization (CACO), a Genetic Algorithm (GA), and Artificial Bee Colony (ABC), with elitism and localized mutation to balance exploration and exploitation. Across six benchmark equity markets (S&P 100, FTSE 100, Hang Seng, Nikkei 225, DAX 100, XU100/XU030), the method yields lower mean/variance return error and smaller mean Euclidean distance to the unconstrained efficient frontier than competing settings. On S&P 100, the best configuration achieves VRE ≈ 2.52, MRE ≈ 0.70, and MEUCD ≈ 1e-4. The approach remains competitive on more volatile markets (e.g., DAX, Nikkei), indicating robustness.  

Keywords:

Portfolio optimization, Cardinality constraint, Metaheuristics, Ant colony optimization, Genetic algorithms, Artificial bee colony

References

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Published

2026-03-26

Issue

Vol. 2 No. 1 (2026): Annals of Optimization With Applications (ANOWA)

Section

Articles

How to Cite

Noripour, M. H. . (2026). Hybrid Metaheuristic Design for Solving the Cardinality-Constrained Portfolio Problem: Comparative Analysis in Six Equity Markets. Annals of Optimization With Applications, 2(1), 91-98. https://doi.org/10.48314/anowa.v2i1.69

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