A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network

Kim Soon Gan, and Chin Kim On, and Rayner Alfred, and A. Patricia, and J. Teo, A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network. Journal of Telecomunication Electronic and Computer Engineering, 10 (3-2). pp. 89-94. ISSN 2289-8131

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Abstract

Artificial Neural Network (ANN) is one of the popular techniques used in stock market price prediction. ANN is able to learn from data pattern and continuously improves the result without prior information about the model. The two popular variants of ANN architecture widely used are Feedforward Neural Network (FFNN) and Recurrent Neural Network (RNN). The literature shows that the performance of these two ANN variants is studied dependent. Hence, this paper aims to compare the performance of FFNN and RNN in predicting the closing price of CIMB stock which is traded on the Kuala Lumpur Stock Exchange (KLSE). This paper describes the design of FFNN and RNN and discusses the performances of both ANNs.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Network, Feedforward, Stock Prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: FACULTY > Faculty of Computing and Informatics
Depositing User: MDM SITI AZIZAH IDRIS
Date Deposited: 12 Mar 2020 08:33
Last Modified: 18 Jun 2020 16:25
URI: http://eprints.ums.edu.my/id/eprint/25233

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