English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70585/70585 (100%)
Visitors : 23192770      Online Users : 504
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/987654321/50075

    Title: Developing an SVM based risk hedging prediction model for construction material suppliers
    Authors: Chen,JH;Lin,JZ
    Contributors: 營建管理研究所
    Date: 2010
    Issue Date: 2012-03-27 17:02:54 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Construction material suppliers are usually exposed to financial risks as a consequence of a high debt capital structure and the nature of the material import business. There is demand for a tool that is able to predict whether such a material supplier, based on its financial status, should use derivatives to hedge financial risks. The research objective is to develop a prediction model using the Support Vector Machine (SVM) to determine whether employing risk hedging based on derivatives usage would be beneficial. The scope of this research limits the database to 640 financial statements published over the last 5 years from 32 listed construction material suppliers. A total of 10 input determinants were identified and verified from the literature review, t-test results, and collinearity diagnosis. Using data trimming and normalization, these 640 sets were downsized to 520 sets which contained 248 effective and 272 ineffective risk-hedging sets. The SVM prediction model, based on the kernel radial basis function and normalized data, yields a prediction accuracy rate of 80.65%. The evaluation, using logistics and small sets of data, shows the validation and practicality of this model. This research concludes that 10 financial determinates are proven candidates for financial risk hedging. From the viewpoint of derivatives usage and the proposed SVM prediction model it appears feasible for construction material suppliers to apply this model. (C) 2010 Elsevier B.V. All rights reserved.
    Appears in Collections:[營建管理研究所 ] 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