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

摘要： 在不断变化的金融市场中，多阶段投资组合优化通过周期性地重组投资对象来追求回报最大，风险最小。提出了使用基于量子化行为的粒子群优化算法（Quantum-behaved Particle Swarm Optimization，QPSO）解决多阶段投资优化问题，并使用经典的利润风险函数作为目标函数，通过算法对标准普尔指数100的不同股票和现金进行投资组合的优化研究。根据实验得出的期望收益率与方差表明，QPSO算法在寻找全局最优解方面要优于粒子群算法（Particle Swarm Optimization，PSO）和遗传算法（Genetic Algorithm，GA）。

关键词: 随机规划, 资产分配, 粒子群, 量子行为