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