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

  • 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.

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  • 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