关键词: 随机规划, 资产分配, 粒子群, 量子行为

Abstract: A multistage stochastic financial 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 uses our proposed Quantum-behaved Particle Swarm Optimization（QPSO） Algorithm to solve multi-stage portfolio optimization problem.The objective function is classical return-variance function.The performance of our algorithm is demonstrated by optimizing the allocation of cash and various stocks in S&P 100 index.Experiments are conducted to compare performance of the portfolios optimized by different objective functions with Particle Swarm Optimization（PSO） algorithm and Genetic Algorithm（GA） in terms of efficient frontiers.

Key words: Multi-objective programming, asset allocation, Particle Swarm, Quantum-behaved