• DocumentCode
    2654682
  • Title

    Research on Risk Early-Warning System of Social Security Fund Investment

  • Author

    Hong, SHEN ; Hong, YU

  • Author_Institution
    Shanghai Univ. of Finance & Econ., Shanghai
  • fYear
    2007
  • fDate
    20-22 Aug. 2007
  • Firstpage
    2040
  • Lastpage
    2045
  • Abstract
    The social security fund will meet with many risks just like other investors when it enters the capital market. Because the social security is linked to everyone´s life, it can not endure heavy losses. As a result, it is very necessary to research how to keep those investment risks away. This paper researches how to establish the early warning system of the investment risk of the social security fund, The goal is to avoid the heavy losses of the fund while invested in the capital market. Mean while it analyzes the systematic risk and non-systematic risk that the social security fund may encounter in the investment process, and then finds out where the relevant risks come from: the market environment, the administration structure of investment and the listed company invested. The early warning system is set up on the early warning index system of risks resources, which can be established by combining the Back propagation artificial neural network with multivariable factor analysis method.
  • Keywords
    backpropagation; investment; multivariable systems; neural nets; risk management; backpropagation artificial neural network; capital market; early warning index system; multivariable factor analysis; risk early-warning system; social security fund investment; Aging; Alarm systems; Artificial neural networks; Conference management; Finance; Financial management; Investments; National security; Risk analysis; Safety; BP neural network model; investment risk; social security fund;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2007. ICMSE 2007. International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-7-88358-080-5
  • Electronic_ISBN
    978-7-88358-080-5
  • Type

    conf

  • DOI
    10.1109/ICMSE.2007.4422140
  • Filename
    4422140