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Particle Swarm Optimization for Constrained Financial Portfolio Selection: An Empirical Study on the US Market

Author

Listed:
  • Abdallah Saib

    (University Center El Bayadh (Algeria))

  • Aboubakr Boussalem

    (University Center El Bayadh (Algeria))

  • Kadri S. Al-Shakri

    (Ajloun National Private University (Jordan))

Abstract

This study investigates Particle Swarm Optimization (PSO) application to portfolio optimization under realistic investment constraints. Using 48 liquid assets' market data (2019-2024), we compare PSO against classical Markowitz optimization and equal-weight benchmarks. The PSO algorithm incorporates weight limits (20%), sector concentration (40%), volatility targeting (18%), and diversification requirements. Results demonstrate PSO's superior performance with Sharpe ratio of 0.9192 versus 0.7281 for constrained Markowitz and 0.7499 for equal-weight portfolios, achieving 26.2% improvement in risk-adjusted returns.

Suggested Citation

Handle: RePEc:bjm:ijep00:v:8:y:2025:i:02:id:390
DOI: 10.54241/2065-008-002-018
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