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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/858

Title: Prediction of S&P 500 and DJIA Stock Indices using Particle Swarm Optimization Technique
Authors: Majhi, R
Panda, G
Sahoo, G
Panda, A
Choubey, A
Keywords: forecasting theory
mean square error methods
multilayer perceptrons
particle swarm optimisation
stock markets
Issue Date: 2008
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation, CEC, June 1-6, Hongkong, 2008. (IEEE World Congress on Computational Intelligence).
Abstract: The present paper introduces the particle swarm optimization (PSO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.
URI: http://dx.doi.org/10.1109/CEC.2008.4630960
http://hdl.handle.net/2080/858
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