Application of Particle Swarm Optimization in Optimal Asset Allocation
Luu Thi Mai Loc *
Nguyen Trai Specialized Senior High School, Hai Duong, Vietnam.
Lai Nguyen Ngoc Dung
School of Applied Mathematics and Informatics, HUST, Hanoi, Vietnam.
Nguyen Quang Dat
Hanoi University of Science, Vietnam National University, Hanoi, Vietnam.
*Author to whom correspondence should be addressed.
Abstract
Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution.
Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.
Keywords: Particle Swarm Optimization (PSO), optimal asset allocation, investment portfolio, returns and risks, expected returns, risk minimization, nonlinear optimization, non-convex optimization, search space, personal experience (individual best), collective experience (global best)