Predicting Stock Market Index Using Hybrid Intelligence Model

  • Authors

    • Sarah Nadirah Mohd Johari
    • Fairuz Husna Muhamad Farid
    • Nur Afifah Enara Binti Nasrudin
    • Nur Sarah Liyana Bistamam
    • Nur Syamira Syamimi Muhammad Shuhaili
    2018-08-13
    https://doi.org/10.14419/ijet.v7i3.15.17403
  • stock prediction, GARCH, ANN, SVM, Hybrid model
  • Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.

     

     

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  • How to Cite

    Nadirah Mohd Johari, S., Husna Muhamad Farid, F., Afifah Enara Binti Nasrudin, N., Sarah Liyana Bistamam, N., & Syamira Syamimi Muhammad Shuhaili, N. (2018). Predicting Stock Market Index Using Hybrid Intelligence Model. International Journal of Engineering & Technology, 7(3.15), 36-39. https://doi.org/10.14419/ijet.v7i3.15.17403