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 Al-gorithm,GA)。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第24期185-188,225,共5页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60474030)
关键词
随机规划资产分配粒子群量子行为
Multi-objective programming
asset allocation
Particle Swarm
Quantum-behaved
