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

  • Abstract

    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.

     

     

  • References

    1. [1] Nawawi, A. H., Radzali, N. H., Hussin, S. A. S., & Mohd, M. A. (2016). Gold hedging strategies in the Asian Markets. Jurnal Teknologi, 78(4-4), 89-98.

      [2] Barro, R.J., &Ursu Ìa, J. F. (2017). Stock-market crashes and depressions. Research in Economics, 71(3), 384-398. https://doi.org/10.1016/j.rie.2017.04.001

      [3] Miah, M.B.A, Hossainm M.Z, Hossain, M.A, Islam. (2015). M.M. Index prediction of stock market using hybrid model of artificial intelligence. International Journal of Computer Applications 111(3):5-9.

      [4] Levy, D. (1994). Chaos theory and strategy: Theory, application and managerial implications. Strategic management journal, 15, 167-167

      [5] Wang, S-C., Wang, P-L., & Fang, K. (2011). To investigate the development process of most popular blog through chaos theory. Paper presented at the ICSSSM11

      [6] Hu, J., Tung, W.-w., Gao, J., & Cao, Y. (2005). Reliability of the 0-1 test for chaos. Physical Review E, 72(5), 056207

      [7] Perwej, Y., Perwej, A.(2012). Prediction of the Bombay stock exchange (BSE) market returns using artificial neural network and genetic algorithm. Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 2, pp. 108-119. doi: 10.4236/jilsa.2012.42010.

      [8] Majumder, M., Hussian MD. (2009). Forecasting of Indian stock market index using artificial neural network. Retrieved from www.nseindia.com/content/research/FinalPaper206.pdf

      [9] Sheta, A.F, Ahmed, S.E.M, Faris, H. (2015). A comparison between regression, artificial neural networks and support vector machines for predicting stock market index. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 4(7).

      [10] Sapini, M. L., Rahim, N. Z. b. A., Noorani, M. S. M., Mohamed, I., How, L. T., Mui, A. C. Y., & Bin, W. K. (2015). The behavior of PM10 and the ozone in Malaysia through nonlinear dynamical systems. Paper presented at the AIP Conference Proceedings

      [11] Shahruddin, S. N. S. S. M., Tafri, F. H. B., & Lazam, N. M. (2012, September). Optimal portfolio:“Empirical investigation of food industry listed in KLSEâ€. In Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on (pp. 1-6). IEEE.

      [12] Azmir, Zahrul & Tafri, Fauziah Hanim. (2012). Islamic options pricing model via artificial neural network: “Benchmarking to Black-Scholesâ€. 1-6. 10.1109/ICSSBE.2012.6396562.

      [13] Tanikic, D., Despotovic, V. (2012). Artificial intelligence techniques for modeling of temperature in the metal cutting process. Metallurgy – Advances in Materials and Processes, Chapter 7

      [14] Patil, S.S, Patidar, K., Jain, M. (2016). Stock market prediction using support vector machine. International Journal of Current Trends in Engineering & Technology, Vol. 02 (1).

      [15] Zhang, G., P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175.

      [16] Siti, S. M., Mohd, M. A., Shima, S. N., & Adzhar, Z. A. A. K. (2017). Value-at-Risk on different economic sectors in Malaysia. Journal of Engineering and Applied Sciences, 12(9), 2289-2293.

      [17] Mohd, M. A. (2011, September). Causality linkages between USA and Asian Islamic stock markets. In Business, Engineering and Industrial Applications (ISBEIA), 2011 IEEE Symposium on (pp. 123-128). IEEE.

  • Downloads

  • 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

    Received date: 2018-08-12

    Accepted date: 2018-08-12

    Published date: 2018-08-13