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政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 > Item 140.119/63946

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题名:	Markowitz-Based Portfolio Selection with Cardinality Constraints Using Improved Particle Swarm Optimization
作者:	林我聰 Deng, Guang-Feng;駱至中;Lin, Woo-Tsong;Lo, Chih-Chung
贡献者:	資管系
关键词:	Particle swarm optimization;Cardinality constrained portfolio optimization problem;Markowitz mean–variance model;Nonlinear mixed quadratic programming problem
日期:	2012-03
上传时间:	2014-02-18 15:17:35 (UTC+8)
摘要:	This work presents particle swarm optimization (PSO), a collaborative population-based meta-heuristic algorithm for solving the Cardinality Constraints Markowitz Portfolio Optimization problem (CCMPO problem). To our knowledge, an efficient algorithmic solution for this nonlinear mixed quadratic programming problem has not been proposed until now. Using heuristic algorithms in this case is imperative. To solve the CCMPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the proposed PSO is much more robust and effective than existing PSO algorithms, especially for low-risk investment portfolios. In most cases, the PSO outperformed genetic algorithm (GA), simulated annealing (SA), and tabu search (TS).
關聯:	Expert Systems with Applications, 39(4), 4558-4566
資料來源:	http://dx.doi.org/10.1016/j.eswa.2011.09.129
数据类型:	article
DOI 連結:	http://dx.doi.org/http://dx.doi.org/10.1016/j.eswa.2011.09.129
DOI:	10.1016/j.eswa.2011.09.129
显示于类别:	[資訊管理學系] 期刊論文

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