Prediction of stock market using cascade correlation neural network with principal component analysis

  • Authors

    • K. Velusamy
    • R. Amalraj
    https://doi.org/10.14419/ijet.v7i4.21723

  • Abstract

    Financial forecasting has gained a significant attention among the researchers and investors.A cascade correlation neural network (CCNN) with principal component analysis (PCA) is developed for financial time series forecasting in this research work.In this paper, the PCA method is used to extract the vital components of the input data, and then the extracted features give the input to the CCNN to carry out the financial time series prediction.A comparison is made with conventional back propagation neural network (BPNN) and CCNN.The em-pirical result shows that the proposed prediction model demonstrates a superior performance in financial time series forecasting.For evalu-ating the performance of the proposed model, the empirical research is applied to well known stock market data sets such as S&P 50 Sensex and Nifty 50.

  • References

    1. [1] G. P. Zhang, "Time series forecasting using a hybrid ARIMA and neural network model," Neurocomputing, vol. 50, pp. 159-175, 2003.https://doi.org/10.1016/S0925-2312(01)00702-0.

      [2] S. Asadi, E. Hadavandi, F. Mehmanpazir, and M. M. Nakhostin, "Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction," Knowledge-Based Systems, vol. 35, pp. 245-258, 2012.https://doi.org/10.1016/j.knosys.2012.05.003.

      [3] M. Khashei and M. Bijari, "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting," Applied Soft Computing, vol. 11, no. 2, pp. 2664-2675, 2011.https://doi.org/10.1016/j.asoc.2010.10.015.

      [4] J.-Z. Wang, J.-J. Wang, Z.-G. Zhang and S.-P. Guo, "Forecasting stock indices with back propagation neural network," Expert Systems with Applications, vol. 38, no. 11, pp. 14346-14355, 2011.

      [5] J. W.-S. Hu, Y.-C. Hu and R. R.-W. Lin, "Applying neural networks to prices prediction of crude oil futures," Mathematical Problems in Engineering, vol. 2012, 2012.

      [6] M. Madhiarasan and S. Deepa, "A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting," Applied intelligence, vol. 44, no. 4, pp. 878-893, 2016.https://doi.org/10.1007/s10489-015-0737-z.

      [7] S. E. Fahlman and C. Lebiere, "The cascade-correlation learning architecture," in Advances in neural information processing systems, 1990, pp. 524-532.

      [8] G. Huang, S. Song, and C. Wu, "Orthogonal least squares algorithm for training cascade neural networks," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 59, no. 11, pp. 2629-2637, 2012.https://doi.org/10.1109/TCSI.2012.2189060.

      [9] J. Nadkarni and R. F. Neves, "Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets," Expert Systems with Applications, vol. 103, pp. 184-195, 2018.https://doi.org/10.1016/j.eswa.2018.03.012.

      [10] J. Wang and J. Wang, "Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks," Neurocomputing, vol. 156, pp. 68-78, 2015.https://doi.org/10.1016/j.neucom.2014.12.084.

      [11] A. Tharwat, "Principal component analysis-a tutorial," International Journal of Applied Pattern Recognition, vol. 3, no. 3, pp. 197-240, 2016.https://doi.org/10.1504/IJAPR.2016.079733.

      [12] R. Amalraj and M. Dharmalingam, "A work point count system coupled with back-propagation for solving double dummy bridge problem," Neurocomputing, vol. 168, pp. 160-178, 2015.https://doi.org/10.1016/j.neucom.2015.06.001.

      [13] K. Velusamy and R. Amalraj, "Performance of the cascade correlation neural network for predicting the stock price," in Electrical, Computer and Communication Technologies (ICECCT), 2017 Second International Conference on, 2017, pp. 1-6: IEEE.

      [14] T. R. Shultz and S. E. Fahlman, "Cascade-correlation," in Encyclopedia of machine learning: Springer, 2011, pp. 139-147.

      [15] J. E. Jackson, A user's guide to principal components. John Wiley & Sons, 2005.

  • Downloads

  • How to Cite

    Velusamy, K., & Amalraj, R. (2018). Prediction of stock market using cascade correlation neural network with principal component analysis. International Journal of Engineering & Technology, 7(4), 3485-3488. https://doi.org/10.14419/ijet.v7i4.21723

    Received date: 2018-11-26

    Accepted date: 2018-11-26