Enhanced set-based particle swarm optimization for portfolio management in a walk-forward paradigm

A novel approach to portfolio optimization is introduced using a variant of set-based particle swarm optimization (SBPSO), building upon the foundational work of Erwin and Engelbrecht. Although their contributions advanced the application of SBPSO to financial markets, this research addresses key practical challenges, specifically enhancing the treatment of covariance and expected returns and refining constraint implementations to align with real-world applications. Beyond algorithmic improvements, this article emphasizes the importance of robust evaluation methodologies and highlights the limitations of traditional backtesting frameworks, which often yield overly optimistic results. To overcome these biases, the study introduces a comprehensive simulation platform that mitigates issues such as survivorship and forward-looking bias. This provides a realistic assessment of the modified SBPSO’s financial performance under varying market conditions. The findings shift the focus from computational efficiency to the practical outcomes of profitability that are most relevant to investors.