Prediction of S&P 500 and DJIA Stock Indices using Particle Swarm Optimization Technique

被引:26
作者
Majhi, Ritanjali [1 ]
Panda, G. [2 ]
Sahoo, G. [3 ]
Panda, Abhishek [4 ]
Choubey, Arvind [5 ]
机构
[1] NIT, Ctr Management Studies, Warangal, Andhra Pradesh, India
[2] Natl Inst Technol, Dept Elect & Commun Engn, Rourkela 769008, India
[3] BIT Mesra, Dept CSE, Ranchi, Bihar, India
[4] UK Bangalore, Bangalore, Karnataka, India
[5] NIT, Dept Elect & Commun Engn, Jamshedpur, Bihar, India
来源
2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 | 2008年
关键词
D O I
10.1109/CEC.2008.4630960
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1276 / +
页数:3
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