• DocumentCode
    2667688
  • Title

    Comparison of the prediction effect between the Logistic Regressive model and SVM model

  • Author

    Sijia, Li ; Lan, Tan ; Yu, Zhuang ; Xiuliang, Yu

  • Author_Institution
    City Coll., Zhejiang Univ., Hangzhou, China
  • fYear
    2010
  • fDate
    17-19 Sept. 2010
  • Firstpage
    316
  • Lastpage
    318
  • Abstract
    Financial crises forewarning has important practical significance both for the investors and for the lenders. This paper uses the financial forewarning models, including the Logistic Regressive model and SVM model, to verify the feasibility of the short-term forecast for the financial situation of enterprises. And the paper also gives comparisons between these two models. The results of the study suggest that these two models are both feasible, and the SVM model can achieve better forecasting effects than the Logistic Regressive model.
  • Keywords
    economic cycles; forecasting theory; regression analysis; support vector machines; SVM model; financial crises; financial forewarning models; investors; logistic regressive model; prediction effect; short-term forecast; Biological system modeling; Companies; Estimation; Logistics; Predictive models; Support vector machines; Training; Credit Risk Management; Logistic Regressive Model; SVM (Supporting Vector Machine) Model; financial crisis forewarning; prediction effect;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-6927-7
  • Type

    conf

  • DOI
    10.1109/ICIFE.2010.5609308
  • Filename
    5609308