English  |  正體中文  |  简体中文  |  Items with full text/Total items : 69561/69561 (100%)
Visitors : 23100481      Online Users : 419
RC Version 7.0 © 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

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29645

    Title: A partitioned portfolio insurance strategy by a relational genetic algorithm
    Authors: Chen,JS;Lin,YT
    Contributors: 資訊管理研究所
    Date: 2009
    Issue Date: 2010-06-29 20:37:14 (UTC+8)
    Publisher: 中央大學
    Abstract: This paper proposes a new portfolio insurance (PI) strategy named partitioned portfolio insurance (PPI) strategy and a new relation-based genetic algorithm named relational genetic algorithm (RGA) to optimize the proposed PPI strategy. Our PPI strategy extends the traditional PI strategy to become a more aggressive one. In our PPI strategy, we attempt to correctly partition the portfolio into several similar sub-portfolios and then insure the sub-portfolios individually. It not only avoids the downside risk, but also further explores the upside profit successfully. In addition, our RGA which adopts a relational encoding and has a set of problem-independent operators is designed to solve the induced portfolio partitioning problem. The relational encoding eliminates the redundancy of previous GA representations for partitioning problems and improves the performance of genetic search. The problem-independent operators we redesigned manipulate the genes without requiring specific heuristics in the process of evolution. Moreover, our RGA works without requiring a preset number of subsets in advance. Experiments for developing optimized PPI strategies by RGA are performed. Experimental results show that our optimized PPI strategies are significantly better than the traditional PI strategy and our RGA works well for solving the portfolio partitioning problem. (C) 2008 Elsevier Ltd. All rights reserved.
    Appears in Collections:[資訊管理研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明