Stock indices prediction using radial basis function neural network

Authors:
Minakhi Rout;Babita Majhi;Usha Manasi Mohapatra;Rosalin Mahapatra
Affiliations:
Dept. of CSE /CA/IT, ITER, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, India;Dept. of CSIT, G.G. Vishwavidyalaya, Central University, Bilaspur, India;Dept. of CSE /CA/IT, ITER, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, India;Dept. of CSE /CA/IT, ITER, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

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Abstract

Aim of the paper is to efficiently predict the stock market data for future days ahead using Radial Basis Function (RBF) neural network. DJIA and S&P 500 stock indices have been taken to simulate the RBF model and also comparison has been done with results obtained from Functional Link Artificial Neural Network(FLANN) and Multilayer Perceptron, (MLP). From the simulation result it is observed that the proposed model is giving better results than other two neural network models interms of prediction accuracy.