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政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 > Item 140.119/63948

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题名:	Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model
作者:	林我聰 Deng, Guang-Feng;Lin, Woo-Tsong
贡献者:	資管系
关键词:	Ant Colony Optimization (ACO);Markowitz mean-variance portfolio model;cardinality constrained portfolio optimization problem;nonlinear mixed quadratic programming problem
日期:	2010-12
上传时间:	2014-02-18 15:18:07 (UTC+8)
摘要:	This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. 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 ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.
關聯:	Lecture Notes in Computer Science, 6466, 238-245
資料來源:	http://link.springer.com/chapter/10.1007%2F978-3-642-17563-3_29
数据类型:	article
DOI 連結:	http://dx.doi.org/10.1007/978-3-642-17563-3_29
DOI:	10.1007/978-3-642-17563-3_29
显示于类别:	[資訊管理學系] 期刊論文

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