Abstract
This paper presents a neural network model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. This model employs Multilayer Feedforward (MLFF) network with Backpropagation (BP) learning. In this article we discuss the modeling of the Indian stock market (price index) data using artificial neural network (ANN). We study the efficacy of ANN in modeling the Bombay Stock Exchange (BSE), Reliance and Oracle data set on closing values. The root mean square error (RMSE) and mean absolute error (MAE) are chosen as indicators of performance of the network. The backpropagation learning algorithm selects a training example, makes a forward and a backward pass, and then repeats until algorithm converges satisfying a pre-specified mean squared error value. The results show the potential of the system as a tool for making stock price prediction.
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References
Groenewold, N., Tang, S.H.K., et al.: The portability of regression-based trading rules for the Shanghai stock market. International Review of Financial Analysis 17, 411–430 (2008)
Medeiros, M.C., Veiga, A., Pedreira, C.E.: Modeling exchange rates: smooth transitions, neural network and linear models. IEEE Trans. Neural Networks 12(4) (2001)
Zhang, B., Coggins, R., Jabri, M., Dersch, D., Flower, B.: Multiresolution forecasting for future trading using wavelet decomposition. IEEE Trans. Neural Networks, special issue in Financial Engg. 12(4), 765–775 (2000)
He, C., Xu, Q., Liu, Y.: A new model between stock valuation index and volatility of stockprice. In: ICICCI 2010, pp. 479–482 (2010)
Huang, K.Y., Jane, C.-J.: A Novel Model for Stock Portfolio based on ARX, RS and a New Grey Relational Grade Theories. In: CIS-RAM 2008 (2008)
Ture, M., Kurt, I.: Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications 31, 41–46 (2006)
Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Chen, C.-H., Lin, C.-J., Lin, C.-T.: Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution. IEEE Trans. Syst. Man Cybern. Part C 39(4), 459–473 (2009)
Chen, S., Leung, M.T., Daouk, H.: Application of Neural Networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computer Operations Res. 30, 901–923 (2003)
Shen, W., Guo, X., Wu, C., Wu, D.: Forecasting Stock Indices using Radial Basis Function Neural Networks Optimized by Artificial Fish Swarm Algorithm. In: Knowledge Based Systems, vol. 24, pp. 378–385 (2011)
Biswal, B., Dash, P.K., Mishra, S.: Non-stationary power signal classification using local linear radial basis function neural networks. International Journal of Knowledge-Based and Intelligent Engineering Systems 13(2) (2009)
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Bebarta, D.K., Rout, A.K., Biswal, B., Dash, P.K. (2012). Efficient Prediction of Stock Market Indices Using Adaptive Neural Network. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_28
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DOI: https://doi.org/10.1007/978-81-322-0491-6_28
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
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