PSO Heuristics Algorithm for Portfolio Optimization
One of the most studied problems in the financial is the intractability of the portfolios. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. In this paper, we present a meta-heuristic algorithm named Particle Swarm Optimization (PSO) to the construction of optimal risky portfolios for financial investments. The PSO algorithm is tested on two portfolio optimization models and a comparative study with Genetic Algorithm has been implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.
KeywordsSwarm Intelligence (SI) Particle Swarm Optimization (PSO) Portfolio Management (PM) Sharp Ratio (SR) Efficient Frontier (EF)
Unable to display preview. Download preview PDF.
- 4.Gaspero, L.D., Tollo, G.d., Roli, A., Schaerf, A.: Hybrid Metaheuristics for Portfolio Selection Problems. In: MIC 2007 - Metaheuristics International Conference, Montreal (2007)Google Scholar
- 6.Giovanis, E.: The Arbitrage Pricing Theory and the Capital Asset Pricing Models and Artificial Neural Networks Modeling with Particle Swarm Optimization (PSO). SSRN eLibrary (2009)Google Scholar
- 7.Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: International Conference on Neural Networks, ICNN 1995, p. 1942 (1995)Google Scholar
- 11.Wang, K.: Applied Computational Intelligence in Intelligent Manufacturing Systems. Advanced Knowledge International (2005)Google Scholar