Solving Multi-period Financial Planning Problem Via Quantum-Behaved Particle Swarm Algorithm
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.
KeywordsParticle Swarm Optimization Planning Horizon Efficient Frontier Market Index Scenario Tree
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