• DocumentCode
    2763493
  • Title

    Stock Returns Prediction Using Manifold Wavelet Kernel

  • Author

    Tang, Ling-Bing ; Sheng, Huan-Ye ; Tang, Ling-Xiao

  • Author_Institution
    Dept. of Comput. & Electron. Eng., Hunan Bus. Coll., Changsha, China
  • fYear
    2009
  • fDate
    6-7 June 2009
  • Firstpage
    306
  • Lastpage
    309
  • Abstract
    An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine (MWSVM) for forecasting stock returns. The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine (SVM). Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities, the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately. The applicability and validity of MWSVM for stock returns forecast is confirmed through experiments on real-world stock data.
  • Keywords
    economic forecasting; financial data processing; function approximation; learning (artificial intelligence); nonlinear functions; pattern classification; regression analysis; stock markets; support vector machines; time series; wavelet transforms; MWSVM; admissible manifold wavelet kernel function; arbitrary nonlinear function approximation; machine learning; pattern classification; regression method; stock return forecasting; stock return prediction; stock time series; support vector machine; Computer science; Economic forecasting; Educational institutions; Electronic commerce; Geometry; Kernel; Manifolds; Support vector machine classification; Support vector machines; Technology forecasting; MWSVM; stock returns;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Commerce and Business Intelligence, 2009. ECBI 2009. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3661-3
  • Type

    conf

  • DOI
    10.1109/ECBI.2009.67
  • Filename
    5190462