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Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques

Das, Debashish and Sadiq, Ali Safa and Noraziah, Ahmad and Lloret, Jaime (2017) Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques. Journal of Multiple-Valued Logic and Soft Computing, 29 (1-2). pp. 157-181. ISSN 1542-3980(print); 1542-3999(online)

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Abstract

The stock market is non-linear in nature, making forecasting a very complicated, challenging and uncertain process. Employing traditional methods may not ensure the reliability of stock prediction. In this paper, we have applied both data mining and optimized neural network in stock prediction with big data. Data mining allows for useful information to be extracted from a huge data set whilst neural network is capable in predicting future trends from large databases; the hybridization of both these techniques can therefore achieve much reliable and robust prediction. This paper has attempted to make a better prediction result for a complicated stock market. In this research, we have collected data from IT Sector organizations of the Dhaka Stock Exchange, which is an emerging stock market and applied K-means clustering of data mining to select the highly increasing securities, Nonlinear autoregressive neural network technique is applied to predict the stock price. The prediction performance through the hybridization is evaluated and positive performance improvement of prediction is observed which is encouraging for investors.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Big data; Forecasting; Electronic trading
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Centre of Excellence: IBM Centre of Excellence
Depositing User: PM Dr. Noraziah Ahmad
Date Deposited: 13 Feb 2018 01:12
Last Modified: 29 Mar 2018 08:02
URI: http://umpir.ump.edu.my/id/eprint/19909
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