An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique

An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique

Objectives: 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 in Bangladeshi sto...

Full description

Bibliographic Details
Main Authors: Das, Debashish, Sadiq, Ali Safa, Noraziah, Ahmad
Format: Article
Language:English
Published: Indian Society for Education and Environment 2016
Subjects:
TN Mining engineering. Metallurgy
Online Access:http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/1/fskkp-2016-aziah-Efficient%20Time%20Series%20Analysis.pdf
id ump-16873
recordtype eprints
spelling ump-168732019-09-10T02:37:34Z http://umpir.ump.edu.my/id/eprint/16873/ 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 TN Mining engineering. Metallurgy Objectives: 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 in Bangladeshi stock market. The objective of this paper is to investigate 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). Methods/Analysis: This study uses daily trade data for Pharmaceutical sector of DSE. 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. Findings: 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. Novelty/Improvement: We intend to apply the data mining and optimized neural network in predicting stock market. We would like to work with the parameter and learning of the neural network to achieve better result. We will further investigate the effect of various factors viz. dollar price, gold price, FDI, bank interest rate etc. on stock price and index movement. Indian Society for Education and Environment 2016-06 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/16873/1/fskkp-2016-aziah-Efficient%20Time%20Series%20Analysis.pdf Das, Debashish and Sadiq, Ali Safa and Noraziah, Ahmad (2016) An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique. Indian Journal of Science and Technology, 9 (21). pp. 1-7. ISSN 0974-6846 http://www.indjst.org/index.php/indjst/article/view/95152 DOI: 10.17485/ijst/2016/v9i21/95152
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TN Mining engineering. Metallurgy
spellingShingle TN Mining engineering. Metallurgy
Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
description Objectives: 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 in Bangladeshi stock market. The objective of this paper is to investigate 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). Methods/Analysis: This study uses daily trade data for Pharmaceutical sector of DSE. 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. Findings: 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. Novelty/Improvement: We intend to apply the data mining and optimized neural network in predicting stock market. We would like to work with the parameter and learning of the neural network to achieve better result. We will further investigate the effect of various factors viz. dollar price, gold price, FDI, bank interest rate etc. on stock price and index movement.
format Article
author Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
author_facet Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
author_sort Das, Debashish
title An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
title_short An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
title_full An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
title_fullStr An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
title_full_unstemmed An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique
title_sort efficient time series analysis for pharmaceutical sector stock prediction by applying hybridization of data mining and neural network technique
publisher Indian Society for Education and Environment
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/
http://umpir.ump.edu.my/id/eprint/16873/1/fskkp-2016-aziah-Efficient%20Time%20Series%20Analysis.pdf
first_indexed 2023-09-18T22:22:55Z
last_indexed 2023-09-18T22:22:55Z
_version_ 1777415776236470272

Similar Items