GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a rapidly growing sector in Bangladeshi stock...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2015
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| Online Access: | http://umpir.ump.edu.my/id/eprint/20938/ http://umpir.ump.edu.my/id/eprint/20938/1/GCMT-189%20An%20Efficient%20Time%20Series%20Analysis%20for%20Pharmaceutical%20Sector%20Stock%20Prediction%20by%20Applying%20Hybridization.pdf |
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ump-20938 |
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eprints |
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ump-209382018-04-04T01:38:25Z http://umpir.ump.edu.my/id/eprint/20938/ GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique Das, Debashish Sadiq, Ali Safa Noraziah, Ahmad The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a rapidly growing sector in Bangladeshi stock market. This paper investigates whether the hybridization of data mining and neural network technique can be applied in predicting the stock price for Pharmaceutical sector of Dhaka Stock Exchange (DSE). This study uses daily trade data for Pharmaceutical sector of DSE. In this paper, we have analysed the behaviour of daily average price for Pharmaceutical sector of DSE. For this study, 6 top listed pharmaceutical companies have been selected to perform the analysis and selected time frame for the research is 15 years (2000-2015). The analysis is performed in two stages where first stage performs the K-means clustering of data mining method to discover the stock with most useful pattern and second stage applies the nonlinear autoregressive with Exogenous Input neural network method to predict the closing price for the selected stock. The prediction performance through the hybridization of data mining and neural network technique is evaluated and positive performance improvement of prediction is observed which is very encouraging for investors. The research also depicts that hybridization of data mining and neural network technique can be applied in determining the stock investment decision for Pharmaceutical sector of DSE though the impact of many different information has greater influence in determining the stock price. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/20938/1/GCMT-189%20An%20Efficient%20Time%20Series%20Analysis%20for%20Pharmaceutical%20Sector%20Stock%20Prediction%20by%20Applying%20Hybridization.pdf Das, Debashish and Sadiq, Ali Safa and Noraziah, Ahmad (2015) GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique. In: Global Congress on Computing and Media Technologies (GCMT’15), 25-27 November 2015 , Chennai, Tamil Nadu, India. pp. 1-8.. |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| description |
The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective
technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a
rapidly growing sector in Bangladeshi stock market. This paper investigates whether the hybridization of data mining and neural
network technique can be applied in predicting the stock price for Pharmaceutical sector of Dhaka Stock Exchange (DSE). This
study uses daily trade data for Pharmaceutical sector of DSE. In this paper, we have analysed the behaviour of daily average price
for Pharmaceutical sector of DSE. For this study, 6 top listed pharmaceutical companies have been selected to perform the
analysis and selected time frame for the research is 15 years (2000-2015). The analysis is performed in two stages where first
stage performs the K-means clustering of data mining method to discover the stock with most useful pattern and second stage
applies the nonlinear autoregressive with Exogenous Input neural network method to predict the closing price for the selected
stock. The prediction performance through the hybridization of data mining and neural network technique is evaluated and
positive performance improvement of prediction is observed which is very encouraging for investors. The research also depicts
that hybridization of data mining and neural network technique can be applied in determining the stock investment decision for
Pharmaceutical sector of DSE though the impact of many different information has greater influence in determining the stock
price. |
| format |
Conference or Workshop Item |
| author |
Das, Debashish Sadiq, Ali Safa Noraziah, Ahmad |
| spellingShingle |
Das, Debashish Sadiq, Ali Safa Noraziah, Ahmad GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| author_facet |
Das, Debashish Sadiq, Ali Safa Noraziah, Ahmad |
| author_sort |
Das, Debashish |
| title |
GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| title_short |
GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| title_full |
GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| title_fullStr |
GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| title_full_unstemmed |
GCMT-189 An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique |
| title_sort |
gcmt-189 an efficient time series analysis for pharmaceutical sector stock prediction by applying hybridization of data mining and neural network technique |
| publishDate |
2015 |
| url |
http://umpir.ump.edu.my/id/eprint/20938/ http://umpir.ump.edu.my/id/eprint/20938/1/GCMT-189%20An%20Efficient%20Time%20Series%20Analysis%20for%20Pharmaceutical%20Sector%20Stock%20Prediction%20by%20Applying%20Hybridization.pdf |
| first_indexed |
2023-09-18T22:30:30Z |
| last_indexed |
2023-09-18T22:30:30Z |
| _version_ |
1777416253146660864 |