Abstract
Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with black-box in nature and lack of providing a formal method of deriving ultimate structure of the model. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities that require less memory in terms of weights and nodes and have been utilized in many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper considered a HONN, called Pi-Sigma Neural Network (PSNN), for prediction of closing prices of five real stock markets. The tunable weights are optimized by Gradient Descent (GD) and a global search technique, Genetic Algorithm (GA). The model proves its superiority when trained with GA in terms of Average Percentage of Errors (APE).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guler, M., Sahin, E.: A new higher-order binary-input neural unit: learning and generalizing effectively via using minimal number of monomials. In: Third Turkish Symposium on Artificial Intelligence and Neural Networks Proceedings, 51–60. Middle East Technical University, Ankara, Turkey (1994)
Wang, Z., Fang, J., and Liu, X.: Global stability of stochastic high-order neural networks with discrete and distributed delays, Chaos, Solutions and Fractals, (2006). doi:10.1016/j.chaos.2006, 06.063
Shin, Y., Ghosh, J.: Ridge polynomial networks. IEEE Trans. Neural Netw. 6, 610–622 (1995)
Park, S., Smith, M.J.T., Mersereau, R.M.: Target recognition based on directional filter banks and higher-order neural networks. Dig. Sign. Process. 10, 297–308 (2000)
Leerink, L.R., Giles, C.L., Horne, B.G., Jabri, M.A.: Learning with product units. In: Tesaro, G., Touretzky, D., Leen, T. (eds.) Advances in neural information processing systems 7, pp. 537–544. MIT Press, Cambridge (1995)
Kwon, Y.K., Moon, B.R.: A hybrid neuro-genetic approach for stock forecasting. IEEE Trans. Neural Netw 18(3), 851–864 (2007)
Yu, L., Zhang, Y. Q.: Evolutionary fuzzy neural networks for hybrid financial prediction. IEEE Trans. Syst Man Cyber Part C Appl Rev 35(2), 244–249 (2005)
Shin, Y., Ghosh, J.: The pi–sigma network: an efficient higher-order neural network for pattern classification and function approximation. In: International Joint Conference on Neural Networks (1991)
Perantonis, S., Ampazis, N., Varoufakis, S., Antoniou, G.: Constrained learning in neural networks: application to stable factorization of 2-d polynomials. Neural Process. Lett. 7(1), 5–14 (1998)
Huang, D.S., Ip, H.H.S., Law, K.C.K., Chi, Z.: Zeroing polynomials using modified constrained neural network approach. IEEE Trans. Neural Netw. 16(3), 721–732 (2005)
Shin, Y., Ghosh, J.: Efficient higher-order neural networks for classification and function approximation. Int. J. Neural Syst. 3, 323–350 (1992)
Epitropakis, M.G., Plagianakos, V.P., Vrahatis, M.N.: Hardware-friendly higher-order neural network training using distributed evolutionary algorithms. Appl. Soft Comput. 10, 398–408 (2010)
Ghazali, R., Husaini, N.H., Ismail, L.H., Samsuddin, N.A.: An Application of jordan pi-sigma neural network for the prediction of temperature time series signal. Recurrent Neural Networks and Soft Computing, (2012) Dr. Mahmoud ElHefnawi (Ed.), ISBN: 978-953-51-0409-4
Ghazali, R., Hussain, A.J., Liatsis, P.: Dynamic ridge polynomial neural network: forecasting the univariate non-stationary and stationary trading signals. Expert Syst. Appl. 38, 3765–3776 (2011). (Elsevier)
Shin Y., Ghosh, J.: Realization of boolean functions using binary pi-sigma networks. In: Dagli, C.H., Kumara, S.R.T., Shin, Y.C. (eds.) Intelligent engineering systems through artificial neural networks, pp. 205–210. ASME Press, New York (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Nayak, S.C., Misra, B.B., Behera, H.S. (2015). A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_29
Download citation
DOI: https://doi.org/10.1007/978-81-322-2208-8_29
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2207-1
Online ISBN: 978-81-322-2208-8
eBook Packages: EngineeringEngineering (R0)