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


  • 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


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


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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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