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Hybrid clustering-GWO-NARX neural network technique in predicting stock price

Das, Debashish and Sadiq, Ali Safa and Mirjalili, Seyedali and Noraziah, Ahmad (2017) Hybrid clustering-GWO-NARX neural network technique in predicting stock price. In: 6th International Conference on Computer Science and Computational Mathematics, ICCSCM 2017, 4-5 May 2017 , Langkawi, Malaysia. pp. 1-14., 892 (012018). ISSN 1742-6596(Print); 1742-6588(Online)

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Abstract

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Stock prediction; Grey wolf optimizer; Non linear autoregressive exogenous algorithm; K-means clustering; Error rate
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: PM Dr. Noraziah Ahmad
Date Deposited: 28 Feb 2018 02:45
Last Modified: 28 Feb 2018 02:51
URI: http://umpir.ump.edu.my/id/eprint/19914
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