Abstract
The central issue of the study is to model the movement of stock price for Indian Information Technology (IT) companies. It has been observed that IT industry has some promising role in Indian economy. We apply the artificial neural networks (ANNs) for modeling purpose. ANNs are flexible computing frameworks and its universal approximations applied to a wide range with desired accuracy. In the study, multilayer perceptron (MLP) models, which are basically feed-forward artificial neural network models, are used for forecasting the stock values of an Indian IT company. On the basis of various features of the network models, an optimal model is being proposed for the purpose of forecasting. Performance measures like \(\text {R}^{2}\), standard error of estimates, mean absolute error, mean absolute percentage error indicate that the model is adequate with respect to acceptable accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Box, G.E.P., Jenkins, G.M.: Time series analysis: forecasting and control. Holden-day Inc., San Francisco (1976)
De Groot, C., Wurtz, D.: Analysis of univariate time series with connectionist nets: a case study of two classical examples. Neurocomputing 3, 177–192 (1991)
El-Hammady, A.H., Abo-Rizka, M.: Neural network based stock market forecasting. IJCSNS 11(8), 204–207 (2011)
Freeman, J.A., Skapura, D.M.: Neural network algorithms, application and programming techniques. Addision Wesley (1991)
Hornick, K., Stinchcombe, M., White, H.: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 3, 551–560 (1990)
Kara, Y., Boyacioglu, M.A., Baykan, O.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Syst. Appl. 38, 5311–5319 (2011)
Manjula, B., Sarma, S.S.V.N., Naik, R.L., Shruthi, G.: Stock Prediction using Neural Network. IJAEST. 10(1), 13–18 (2011)
Merh, N., Saxena, V.P., Pardasani, K.R.: A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Bus. Int. J. 3(2), 23–43 (2010)
Panahian, H.: Stock market index forecasting by neural networks models and nonlinear multiple regression modeling: study of Iran’s capital market. Am. J. Sci. Res. 18, 35–51 (2011)
Qi, M., Zhang, G.P.: An investigation of model selection criteria for neural network time series forecasting. Eur. J. Operat. Res. 132(3), 666–680 (2001)
Rumelhart, D.E., McClelland, J.L., The PDP Research Group: Parallel distributed processing: explorations in the microstructure of cognition, vols. 1–2. MIT Press, Cambridge (1986)
Schoneburg, E.: Stock price prediction using neural network: a project report. Neurocomputing 2(1), 17–27 (1990)
Thenmozhi, M.: Forecasting stock index returns using neural networks. DBR 7(2), 59–69 (2006)
Vashisth, R., Chandra, A.: Predicting stcok returns in nifty index: an application of artificial neural network. IRJFE 49, 15–23 (2010)
Virli, F., Freisleben, B.: Neural network model selection for financial time series prediction. Comput. Stat. 16(3), 451–463 (2001)
Zhang, Y., Wu, L.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert. Syst Appl. 36(5), 8849–8854 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Sen, J., Das, A.K. (2014). Artificial Neural Network Model for Forecasting the Stock Price of Indian IT Company. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_121
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_121
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)