• DocumentCode
    1637602
  • Title

    Short-term prediction of Shanghai composite index based on SVM

  • Author

    Wang, Xiaoyun ; Lin, Limin

  • Author_Institution
    Inst. of Manage. Sci. & Inf. Eng., Hangzhou Dianzi Univ., Hangzhou, China
  • fYear
    2010
  • Firstpage
    170
  • Lastpage
    173
  • Abstract
    Technical indicators are very important tools in the analysis of securities investment. Closing prices and volume of business are basic index, and they compose many complex technical index. In this paper, we represent the daily closing prices and daily volume of business as input vector, and construct 9 projects according different input vector. After 9 contrast experiments with support vector machines, we find that daily closing prices and daily volume of business have 3 days of time validity in predicting future stock price.
  • Keywords
    financial data processing; stock markets; support vector machines; Shanghai composite index; daily business volume; daily closing prices; future stock price; securities investment analysis; short-term prediction; support vector machines; Artificial neural networks; Business; Forecasting; Indexes; Kernel; Stock markets; Support vector machines; short-term prediction; support vector machine; technical indicator; time validity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-6054-0
  • Type

    conf

  • DOI
    10.1109/ICSESS.2010.5552390
  • Filename
    5552390
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1637602