IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-319-72745-5_28.html
   My bibliography  Save this book chapter

A Study on Assets Categorizations and Optimal Allocation via an Improved Algorithm

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • Guang Liu

    (Guangzhou University)

Abstract

The efficiency of assets allocation is essential to investment performance. To deal with the dynamic optimization of industry asset allocation, an improved algorithm (the Index Hierarchical Structure Algorithm, IHSA) is applied to 20 industry indexes according to different sample period, and proved to deduce stabilized industry categorizations. Then, a series of different size industry portfolios are constructed by the algorithm, and their monthly rate of returns are compared with 369 open-ended funds (including 263 growth funds and 106 index funds). The results indicate that the performance of each industry portfolio is better than most of the sample funds in the early sample period, especially than index funds. The algorithm provides a useful guidance for industry allocation and active asset management.

Suggested Citation

  • Guang Liu, 2018. "A Study on Assets Categorizations and Optimal Allocation via an Improved Algorithm," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 255-264, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_28
    DOI: 10.1007/978-3-319-72745-5_28
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-319-72745-5_28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.