Hybrid clustering-GWO-NARX neural network technique in predicting stock price
Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent....
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| Language: | en |
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IOP Publishing Ltd
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19914/1/Hybrid%20Clustering%20GWO%20NARX%20neural.pdf http://umpir.ump.edu.my/id/eprint/19914/ http://iopscience.iop.org/article/10.1088/1742-6596/892/1/012018/meta |
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| author | Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Noraziah, Ahmad |
| author_facet | Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Noraziah, Ahmad |
| author_sort | Das, Debashish |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.19914 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2017 |
| publisher | IOP Publishing Ltd |
| record_format | eprints |
| spelling | my.ump.umpir.199142018-02-28T02:51:16Z http://umpir.ump.edu.my/id/eprint/19914/ Hybrid clustering-GWO-NARX neural network technique in predicting stock price Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Noraziah, Ahmad QA75 Electronic computers. Computer science Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices. IOP Publishing Ltd 2017 Conference or Workshop Item PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/19914/1/Hybrid%20Clustering%20GWO%20NARX%20neural.pdf Das, Debashish and Sadiq, Ali Safa and Mirjalili, Seyedali and Noraziah, Ahmad (2017) Hybrid clustering-GWO-NARX neural network technique in predicting stock price. In: 6th International Conference on Computer Science and Computational Mathematics, ICCSCM 2017 , 4-5 May 2017 , Langkawi, Malaysia. pp. 1-14., 892 (012018). ISSN 1742-6596(Print); 1742-6588(Online) (Published) http://iopscience.iop.org/article/10.1088/1742-6596/892/1/012018/meta |
| spellingShingle | QA75 Electronic computers. Computer science Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Noraziah, Ahmad Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title | Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title_full | Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title_fullStr | Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title_full_unstemmed | Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title_short | Hybrid clustering-GWO-NARX neural network technique in predicting stock price |
| title_sort | hybrid clustering-gwo-narx neural network technique in predicting stock price |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/19914/1/Hybrid%20Clustering%20GWO%20NARX%20neural.pdf http://umpir.ump.edu.my/id/eprint/19914/ http://iopscience.iop.org/article/10.1088/1742-6596/892/1/012018/meta |
| url_provider | http://umpir.ump.edu.my/ |