PQPSO Algorithm in Multi-Stage Portfolio Optimization System

Portfolio optimization problem decides the percentage of the overall portfolio value allocated to each portfolio component with specified risk-return characteristics. A multistage optimization manages portfolio in constantly changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. In this paper, we present a decision-making process that incorporates parallel quantum particle swarm optimization (PQPSO) Algorithm into multi-stage portfolio optimization system. The objective function is to maximize one´s economic utility or end-of-period wealth. The performance of our algorithm is demonstrated by optimizing the allocation of cash and various stocks in Shanghai market of China. Experiments are conducted to compare performance of the portfolios optimized by different objective functions in terms of expected return and standard derivation.