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Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique

Das, Debashish and Sadiq, Ali Safa and Mirjalili, Seyedali (2017) Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique. In: The 6th International Conference on Computer Science and Computational Mathematics (ICCSCM 2017), 4-5 May 2017 , Langkawi, Malaysia. pp. 1-8..

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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.

Item Type: Conference or Workshop Item (Lecture)
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: Noorul Farina Arifin
Date Deposited: 29 Mar 2018 08:24
Last Modified: 30 Mar 2018 08:17
URI: http://umpir.ump.edu.my/id/eprint/20937
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