Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

  • Authors

    • Said Jadid Abdulkadir
    • Hitham Alhussian
    • Muhammad Nazmi
    • Asim A Elsheikh
    2018-10-07
    https://doi.org/10.14419/ijet.v7i4.15.21365
  • Financial Time-Series, Long Short Term Memory, Recurrent Network, Standard and Poor’s 500 Index.

  • Abstract

    Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.

     

     

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  • How to Cite

    Jadid Abdulkadir, S., Alhussian, H., Nazmi, M., & A Elsheikh, A. (2018). Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling. International Journal of Engineering & Technology, 7(4.15), 25-29. https://doi.org/10.14419/ijet.v7i4.15.21365

    Received date: 2018-10-09

    Accepted date: 2018-10-09

    Published date: 2018-10-07