English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23474954      Online Users : 263
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/63946
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/63946


    Title: Markowitz-Based Portfolio Selection with Cardinality Constraints Using Improved Particle Swarm Optimization
    Authors: 林我聰
    Deng, Guang-Feng;駱至中;Lin, Woo-Tsong;Lo, Chih-Chung
    Contributors: 資管系
    Keywords: Particle swarm optimization;Cardinality constrained portfolio optimization problem;Markowitz mean–variance model;Nonlinear mixed quadratic programming problem
    Date: 2012-03
    Issue Date: 2014-02-18 15:17:35 (UTC+8)
    Abstract: 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).
    Relation: Expert Systems with Applications, 39(4), 4558-4566
    Source URI: http://dx.doi.org/10.1016/j.eswa.2011.09.129
    Data Type: 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
    Appears in Collections:[資訊管理學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    6961.pdf647KbAdobe PDF923View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback