Effectiveness of Artificial Neural Networks in Solving Financial Time Series

  • Authors

    • Marwan Abdul Hameed Ashour
    • Arshad Jamal
    • Rabab Alayham Abbas Helmi
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.11.20783
  • Back propagation, general regression network, GARCH model, Heteroscedastic variance financial series, neural networks.

  • Abstract

    This research aims to study and analyze which type of Artificial Neural Network (ANN) is more efficient and suitable in handling non-homogenous variance for financial series. Apart from addressing the behavior and efficiency of ANN, the paper also aims to present an advanced methodology for ANN, as a replacement of GARCH and ARCH models in crisis management decision makers. The application part was applied to the Egyptian exchange market, to study the local currency exchange rate volatility (1/1/2009-4/6/2013) in order to develop a model describing those changes in the exchange rate. The research concludes that the best network type to represent such financial series is the Back Propagation. Moreover, comparing the result with general regression and probabilistic networks rendered the later two inefficient at handling such series.

     

  • References

    1. [1] G. Bekaert, and R. Hodrick, International financial management. Cambridge University Press, 2017.

      [2] M. J. Muhammed, "The use of GARCH model for Saudi financial market forecasting," Proceedings of the Al-Rafidain University Second Scientific Conference, 2010.

      [3] R. S. Tsay, Analysis of financial time series. John Wiley and Sons, 2005.

      [4] R. Engle, "Risk and volatility: Econometric models and financial practice," American Economic Review, 94(3), 405-420, 2004.

      [5] S. Benkachcha. J. Benhra, and H. El Hassani, "Seasonal time series forecasting model based on artificial neural network," International Journal of Computer Applications, 116(20), 9-14, 2015.

      [6] A. K. Dhamijam, and V. K. Bhalla, "Financial time series forecasting: Comparison of neural networks and ARCH models," International Research Journal of Finance and Economics, 49, 185-202, 2010.

      [7] O. Erdogan and A. Goksu, "Forecasting Euro and Turkish Lira exchange rates with Artificial Neural Networks (ANN)," International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(4), 307-316, 2014.

      [8] S. Malik and A. K. Bhatt, "Developing a model for financial forecasting through artificial neural networks,"

      [9] O. Stetter, D. Battaglia, J. Soriano, and T. Geisel, "Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals," Plos Computational Biology, 8, 1-26, 2012.

      [10] M. A. Ashour, and R. A. Abbas, “Improving time series' forecast errors by using recurrent neural networks,†Proceedings of the ACM 7th International Conference on Software and Computer Applications, pp. 229-232, 2018.

  • Downloads

  • How to Cite

    Abdul Hameed Ashour, M., Jamal, A., & Alayham Abbas Helmi, R. (2018). Effectiveness of Artificial Neural Networks in Solving Financial Time Series. International Journal of Engineering & Technology, 7(4.11), 99-105. https://doi.org/10.14419/ijet.v7i4.11.20783

    Received date: 2018-10-02

    Accepted date: 2018-10-02

    Published date: 2018-10-02