Synchronous Set-Based Particle Swarm Optimization: Heuristics for Portfolio Optimization
Ashish Lakhmani, Ruppa K. Thulasiram, Parimala Thulasiraman · 2024
Portfolio optimization (PO) difficulties entail deciding which assets to invest and allocating the weights in those assets in order to maximize overall return while minimizing overall risk at the same time. With an increase in the vast number of assets available to invest, the stock selection and optimal asset weight allocation becomes more complex. In recent studies, researchers have achieved better performance in asset selection and weight allocation to an extent using nature-inspired algorithms than traditional methods often at the cost of heavy computing power used in blending multiple methods, or considering a small pool of assets. In this study, we propose a novel heuristics, which we call synchronous set-based particle swarm optimization (SSBPSO), that performs a large scale stock selection and weight optimization to generate resilient portfolios from a large pool of assets. The portfolios are generated from the pool of stocks that are constituents of stock indexes and their performance is compared with the indexes itself. We used three stock indexes from around the world and generated portfolios using SSBPSO, the returns of portfolio generated outperform the stock indexes in terms of portfolio return.