• DocumentCode
    2387423
  • Title

    Use of Neural Networks in Forecasting Financial Market

  • Author

    Marzi, Hosein ; Turnbull, Mark

  • Author_Institution
    St. Francis Xavier Univ., Antigonish
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    516
  • Lastpage
    516
  • Abstract
    In today´s volatile financial market the demand for an accurate option price forecaster has been a focal point for researchers. The purpose of this study is to forecast option prices using neural networks. Initially simple neural network was implemented using twenty year period data from S&P 500 index call option prices. The prediction result was better than that of traditional Black-Scholes model. A hybrid neural network was developed that utilized aspects of Black- Scholes model into the neural network and tested against the traditional approach and simple neural network. The hybrid neural network outperformed performance of the tradition forecasting model and improved prediction results of simple neural networks.
  • Keywords
    backpropagation; economic forecasting; neural nets; share prices; stock markets; Black-Scholes model; Levenberg-Marquardt backpropagation algorithm; financial market forecasting; hybrid neural network training; option prices; Artificial neural networks; Computer networks; Demand forecasting; Economic forecasting; Economic indicators; Information systems; Neural networks; Predictive models; Pricing; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing, 2007. GRC 2007. IEEE International Conference on
  • Conference_Location
    Fremont, CA
  • Print_ISBN
    978-0-7695-3032-1
  • Type

    conf

  • DOI
    10.1109/GrC.2007.78
  • Filename
    4403153