• DocumentCode
    3406214
  • Title

    Grey clustering statistic, policyholder´s risk attitude and purchase decision

  • Author

    Ker-tah, Hsu ; Weiling, Liu ; Tzung-ming, Yan

  • Author_Institution
    Dept. of Int. Bus., Nat. Taichung Univ., Taichung, Taiwan
  • fYear
    2009
  • fDate
    10-12 Nov. 2009
  • Firstpage
    1126
  • Lastpage
    1131
  • Abstract
    For life insurance companies, selecting the potential buyers of investment-linked insurance out of the existing policyholders is an effective and economic strategy. We try to use logistic regression model combined with grey clustering statistic to forecast the policyholder´s purchase decision of investment-inked Insurance. Because policyholder of investment-linked insurance bears the investment risk, their risk attitude should have a great impact on their purchase decision of investment-linked insurance. We take general risk attitude and financial risk attitude into account at the same time. Grey clustering statistic offers an alternative to the traditional methods of classification for risk attitude. The accuracy ratio of our model is 79.11%. Finally, we find that financial risk attitude is more relevant for policyholders´ purchase decision than general risk attitude.
  • Keywords
    decision theory; grey systems; insurance; investment; regression analysis; risk analysis; economic strategy; financial risk attitude; general risk attitude; grey clustering statistic; investment linked insurance policy; investment risk; life insurance companies; logistic regression model; policyholder; purchase decision; Cultural differences; Demography; Economic forecasting; Instruments; Insurance; Investments; Mutual funds; Psychology; Social factors; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-4914-9
  • Electronic_ISBN
    978-1-4244-4916-3
  • Type

    conf

  • DOI
    10.1109/GSIS.2009.5408080
  • Filename
    5408080