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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 13-Jul-2015 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
dc.contributor 資管系-
dc.creator (作者) Deng, Guang-Feng;Chen, Chuen-Lung-
dc.creator (作者) 鄧廣豐;陳春龍-
dc.date (日期) 2007-
dc.date.accessioned 13-Jul-2015 16:05:30 (UTC+8)-
dc.date.available 13-Jul-2015 16:05:30 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2015 16:05:30 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76510-
dc.description.abstract (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).-
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings of the International Conference on Electronic Business (ICEB)-
dc.relation (關聯) 7th International Conference on Electronic Business, ICEB 2007,2 December 2007 through 6 December 2007,Taipei-
dc.subject (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-
dc.title (題名) Applying particle swarm optimization to solve portfolio selection problems-
dc.type (資料類型) conferenceen