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


  • Faculty of Computer Systems and Software Engineering, University Malaysia Pahang Kuantan, Pahang Darul Makmur, Malaysia


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.


Data Mining, Dhaka Stock Exchange, Hybridization, K-means Clustering, Neural Network, Nonlinear Autoregressive Neural Network, Pharmaceutical Sector, Stock Prediction.

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  • Dingxian Wang, Xiao Liu and Mengdi Wang. A DT-SVM Strategy for Stock Futures Prediction with Big Data. Sydney, NSW: IEEE 16th International Conference on Computational Science and Engineering. 2013; p. 1005–12.
  • Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C and Wirth R. CRISP-DM 1.0: Step-by-step data mining guide. NCR Systems Engineering Copenhagen (USA and Denmark), Daimler Chrysler AG (Germany), SPSS Inc. (USA) and OHRA Verzekeringenen Bank Group B.V. (The Netherlands). 2000.
  • Sang C Suh, Ed. Jones & Bartlett Learning, LLC: Practical Applications of Data Mining. 2012.
  • Rai Pratyoosh and Rai Kajal. Comparison of Stock Prediction Using Different Neural Network Types. International Journal of Advanced Engineering & Application. 2011 Jan; p. 157–60.
  • Contreras J, Espinola R, Nogales FJ and Conejo AJ. ARIMA models to predict Next Day Electricity Prices. IEEE Transactions on power system. 2003; 18(3):1014–20.
  • Armano G, Marchesi M and Murru A. A hybrid genetic-neural architecture for stock indexes forecasting. International Journal of Information Science. 2005; 170:3–33.
  • Dase RK. and Pawar DD. Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence. 2010; 2(2):14–17.
  • Wong Bodnovich and Selvi. Neural Network application. Neural Network Business. 1997; 19:301–20.
  • Deshpande SP, Thakare VM. Datamining System And Applications: A Review. International Journal of Distributed and Parallel systems (IJDPS). 2010; 1(1):32–44.
  • Isfan Monica, Menezes Rui and Diana A Mendes. Forecasting the portuguese stock market time series by using artificial neural networks. Journal of Physics, 7th International Conference on Applications of Physics in Financial Analysis. 2010; 221:1–14.
  • Adebiyi Ayodele A, Ayo Charles K, Adebiyi Marion O and Otokiti Sunday O. Stock Market Prediction using Neural Network with Hybridized Market Indicators. Journal of Emerging Trends in Computing and Information Sciences. 2011; 3(1):1–9.
  • Khan Zabir Haider, Alin Tasnim Sharmin and Hussain Md. Akter. Price Prediction of Share Market using Artificial Neural Network, (ANN). International Journal of Computer Applications. 2011; 22(2):42–47.
  • MathWorks. Date Accessed: 1/09/2015: Available from:
  • Callan Robert. Ed. Prentice Hall Europe: The Essence of Neural Networks. 1999.
  • Isfan Monica, Menezes Rui and Diana A. Mendes. Journal of Physics. 7th International Conference on Applications of Physics in Financial Analysis. 2010; 221:1–14.
  • Santhanam T, Ephzibah EP. Heart Disease Prediction Using Hybrid Genetic Fuzzy Model. Indian Journal of Science and Technology. 2015; 8(9):797–803.
  • Heidarpoor Farzaneh, Shahrivar Farhad Sheikhi. Unsystematic Risk and Internal Control Quality Impact on the Earning Quality by using Volatility Profits Index in Tehran Stock Exchange. Indian Journal of Science and Technology. 2015; 8(11):1–6.


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