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Solving Multi-period Financial Planning Problem Via Quantum-Behaved Particle Swarm Algorithm

  • Jun Sun
  • Wenbo Xu
  • Wei Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

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.

Keywords

Particle Swarm Optimization Planning Horizon Efficient Frontier Market Index Scenario Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Sun
    • 1
  • Wenbo Xu
    • 1
  • Wei Fang
    • 1
  1. 1.Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 JiangsuChina

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