Prediction of the stock market data is a challenging task to make more profit for investor and customers. The artificial neural network has been applied to predict the stock market data in order to obtain more profit on the right time with efficient manner. The conventional neural network learning algorithm has been producing low prediction performance due to the high level of uncertainty in the stock market data. Hence, the fuzzy cascade correlation neural network has been applied to predict future behavior of the stock market index. The simulation result demonstrates that the proposed method shows high generalization performance and produced higher prediction accuracy

Fuzzy Neural Network, Cascade Correlation Neural Networks, Back Propagation Neural Networks, Stock Index Prediction.

[1] C.-H. Cheng and J.-H. Yang, "Fuzzy time-series model based on rough set rule induction for forecasting stock price," Neurocomputing, vol. 302, pp. 33-45, 2018.
[2] H. Guan, Z. Dai, A. Zhao, and J. He, "A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network," PloS one, vol. 13, p. e0192366, 2018.
[3] 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, pp. 878-893, 2016.
[4] S. E. Fahlman and C. Lebiere, "The cascade-correlation learning architecture," in Advances in neural information processing systems, 1990, pp. 524-532.
[5] E. Hadavandi, H. Shavandi, and A. Ghanbari, "Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting," Knowledge-Based Systems, vol. 23, pp. 800-808, 2010.
[6] L. A. Zadeh, "On fuzzy algorithms," in Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers by Lotfi A Zadeh, ed: World Scientific, 1996, pp. 127-147.
[7] R. J. Kuo, C. Chen, and Y. Hwang, "An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network," Fuzzy sets and systems, vol. 118, pp. 21-45, 2001.
[8] H. Ishibuchi, K. Kwon, and H. Tanaka, "A learning algorithm of fuzzy neural networks with triangular fuzzy weights," Fuzzy sets and systems, vol. 71, pp. 277-293, 1995.
[9] S. Wu, M. J. J. I. T. o. S. Er, Man,, and P. B. Cybernetics, "Dynamic fuzzy neural networks-a novel approach to function approximation," vol. 30, pp. 358-364, 2000.
[10] 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, pp. 2629-2637, 2012.
[11] T. R. Shultz and S. E. Fahlman, "Cascade-correlation," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 139-147.
[12] Y. V. Bodyanskiy, O. K. Tyshchenko, and D. S. Kopaliani, "Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks," Evolving Systems, vol. 7, pp. 107-116, 2016.
[13] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control: John Wiley & Sons, 2015.
[14] Rohit Kumar, Rohit Gajbhiye, Isha Nikhar, and Dyotak Thengdi, Sofia Pillai, “Stock Market Prediction using Deep Neural Networks”, International Journal of Computer Sciences and Engineering, Vol.-7, Issue-4, April 2019