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    題名: Applying particle swarm optimization to solve portfolio selection problems
    作者: Deng, Guang-Feng;Chen, Chuen-Lung
    鄧廣豐;陳春龍
    貢獻者: 資管系
    關鍵詞: Computational results;Efficient frontier;Evolutionary computation techniques;Library data;Markowitz;Mean variance;Mean-variance approach;Optimization problems;Particle swarm;Portfolio selection;Portfolio selection problems;Search Algorithms;Electronic commerce;Genetic algorithms;Particle swarm optimization (PSO);Simulated annealing;Tabu search;Problem solving
    日期: 2007
    上傳時間: 2015-07-13 16:05:30 (UTC+8)
    摘要: Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a social population-based search algorithm and is generally similar to the evolutionary computation techniques that have been successfully applied to solve various hard optimization problems. The standard Markowitz mean-variance approach to portfolio selection involves tracing out an efficient frontier, a continuous curve illustrating the tradeoff between return and risk. In this paper we applied the particle swarm approach to find an efficient frontier associated with the classical and general (unconstrained and constrained) mean-variance portfolio selection problem. The OR library data sets were tested in our paper and computational results showed that the PSO found better solutions when compared to genetic algorithm (GA), simulated annealing(SA), and tabu search(TS).
    關聯: Proceedings of the International Conference on Electronic Business (ICEB)
    7th International Conference on Electronic Business, ICEB 2007,2 December 2007 through 6 December 2007,Taipei
    資料類型: conference
    顯示於類別:[資訊管理學系] 會議論文

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