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

Authors

  • G. Kim Soon Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
  • C. Kim On Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
  • A. Rayner Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
  • A. Patricia Faculty of Environment, Society and Design, Lincoln University, Christchurch, New Zealand.
  • J. Teo Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia

Keywords:

Artificial Neural Network, Feedforward Neural Network, Recurrent Neural Network, Stock Prediction,

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.

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Published

2018-09-26

How to Cite

Kim Soon, G., Kim On, C., Rayner, A., Patricia, A., & Teo, J. (2018). A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2), 89–94. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4717

Issue

Vol. 10 No. 3-2: Recent Trends in Computer Science and Information Technology

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)