A quick gbest guided artificial bee colony algorithm for stock market prices prediction
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 t...
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| Format: | Article |
| Language: | en |
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2018
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| Online Access: | http://eprints.uthm.edu.my/4141/1/AJ%202018%20%28730%29%20A%20quick%20gbest%20guided%20artificial%20bee%20colony%20algorithm%20for%20stock%20market%20prices%20prediction.pdf http://eprints.uthm.edu.my/4141/ |
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| author | Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida |
| author_facet | Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida |
| author_sort | Shah, Habib |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-4141 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | MDPI |
| record_format | eprints |
| spelling | my.uthm.eprints-41412021-11-25T04:11:59Z http://eprints.uthm.edu.my/4141/ A quick gbest guided artificial bee colony algorithm for stock market prices prediction Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida QA76 Computer software T58.6-58.62 Management information systems 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. MDPI 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4141/1/AJ%202018%20%28730%29%20A%20quick%20gbest%20guided%20artificial%20bee%20colony%20algorithm%20for%20stock%20market%20prices%20prediction.pdf Shah, Habib and Tairan, Nasser and Garg, Harish and Ghazali, Rozaida (2018) A quick gbest guided artificial bee colony algorithm for stock market prices prediction. Symmetry, 10 (292). pp. 1-15. ISSN 2073-8994 |
| spellingShingle | QA76 Computer software T58.6-58.62 Management information systems Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title | A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title_full | A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title_fullStr | A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title_full_unstemmed | A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title_short | A quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| title_sort | quick gbest guided artificial bee colony algorithm for stock market prices prediction |
| topic | QA76 Computer software T58.6-58.62 Management information systems |
| url | http://eprints.uthm.edu.my/4141/1/AJ%202018%20%28730%29%20A%20quick%20gbest%20guided%20artificial%20bee%20colony%20algorithm%20for%20stock%20market%20prices%20prediction.pdf http://eprints.uthm.edu.my/4141/ |
| url_provider | http://eprints.uthm.edu.my/ |