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題名 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
貢獻者 資管系
關鍵詞 Particle swarm optimization; Cardinality constrained portfolio optimization problem; Markowitz mean–variance model; Nonlinear mixed quadratic programming problem
日期 2012-03
上傳時間 18-Feb-2014 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
資料類型 article
DOI http://dx.doi.org/http://dx.doi.org/10.1016/j.eswa.2011.09.129
dc.contributor 資管系en_US
dc.creator (作者) 林我聰zh_TW
dc.creator (作者) Deng, Guang-Feng;駱至中; Lin, Woo-Tsong; Lo, Chih-Chungen_US
dc.date (日期) 2012-03en_US
dc.date.accessioned 18-Feb-2014 15:17:35 (UTC+8)-
dc.date.available 18-Feb-2014 15:17:35 (UTC+8)-
dc.date.issued (上傳時間) 18-Feb-2014 15:17:35 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63946-
dc.description.abstract (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).en_US
dc.format.extent 663134 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Expert Systems with Applications, 39(4), 4558-4566en_US
dc.source.uri (資料來源) http://dx.doi.org/10.1016/j.eswa.2011.09.129en_US
dc.subject (關鍵詞) Particle swarm optimization; Cardinality constrained portfolio optimization problem; Markowitz mean–variance model; Nonlinear mixed quadratic programming problemen_US
dc.title (題名) Markowitz-Based Portfolio Selection with Cardinality Constraints Using Improved Particle Swarm Optimizationen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.eswa.2011.09.129en_US
dc.doi.uri (DOI) http://dx.doi.org/http://dx.doi.org/10.1016/j.eswa.2011.09.129en_US