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Symmetry 2018, 10(7), 292; https://doi.org/10.3390/sym10070292

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

1
College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
2
School of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala 147004, Punjab, India
3
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat Johor 86400, Malaysia
*
Author to whom correspondence should be addressed.
Received: 21 June 2018 / Revised: 6 July 2018 / Accepted: 10 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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Abstract

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values. View Full-Text
Keywords: Quick Gbest Guided Artificial Bee Colony; financial time series prediction; Saudi stock exchange; natural inspired algorithms Quick Gbest Guided Artificial Bee Colony; financial time series prediction; Saudi stock exchange; natural inspired algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Shah, H.; Tairan, N.; Garg, H.; Ghazali, R. A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction. Symmetry 2018, 10, 292.

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