Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
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....
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf |
| _version_ | 1796992662298951680 |
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| author | Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali |
| author_facet | Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali |
| author_sort | Das, Debashish |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:23:16Z |
| format | Conference or Workshop Item |
| id | UMPir20937 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:23:16Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | UMPir209372018-03-30T08:17:16Z http://umpir.ump.edu.my/id/eprint/20937/ Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali 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. 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf Das, Debashish and Sadiq, Ali Safa and Mirjalili, Seyedali (2017) Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique. In: The 6th International Conference on Computer Science and Computational Mathematics (ICCSCM 2017) , 4-5 May 2017 , Langkawi, Malaysia. pp. 1-8.. |
| spellingShingle | QA75 Electronic computers. Computer science Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title | Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title_full | Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title_fullStr | Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title_full_unstemmed | Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title_short | Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique |
| title_sort | stock prediction by applying hybrid clustering gwo narx neural network technique |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf |
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