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題名 Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model
作者 林我聰
Deng, Guang-Feng; Lin, Woo-Tsong
貢獻者 資管系
關鍵詞 Ant Colony Optimization (ACO);Markowitz mean-variance portfolio model;cardinality constrained portfolio optimization problem;nonlinear mixed quadratic programming problem
日期 2010-12
上傳時間 18-Feb-2014 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
資料類型 article
DOI http://dx.doi.org/10.1007/978-3-642-17563-3_29
dc.contributor 資管系en_US
dc.creator (作者) 林我聰zh_TW
dc.creator (作者) Deng, Guang-Feng; Lin, Woo-Tsongen_US
dc.date (日期) 2010-12en_US
dc.date.accessioned 18-Feb-2014 15:18:07 (UTC+8)-
dc.date.available 18-Feb-2014 15:18:07 (UTC+8)-
dc.date.issued (上傳時間) 18-Feb-2014 15:18:07 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63948-
dc.description.abstract (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.en_US
dc.format.extent 226726 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Lecture Notes in Computer Science, 6466, 238-245en_US
dc.source.uri (資料來源) http://link.springer.com/chapter/10.1007%2F978-3-642-17563-3_29en_US
dc.subject (關鍵詞) Ant Colony Optimization (ACO);Markowitz mean-variance portfolio model;cardinality constrained portfolio optimization problem;nonlinear mixed quadratic programming problemen_US
dc.title (題名) Ant Colony Optimization for Markowitz Mean-Variance Portfolio Modelen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1007/978-3-642-17563-3_29en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-642-17563-3_29en_US