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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.

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