Kim, Soon Gan and On, Chin Kim and Rayner Alfred and A. Patricia and J. Teo (2018) 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.
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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 |
|---|---|
| Keyword: | Artificial Neural Network, Feedforward, Stock Prediction |
| Subjects: | H Social Sciences > HG Finance > HG1-9999 Finance > HG4501-6051 Investment, capital formation, speculation Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science |
| Department: | FACULTY > Faculty of Computing and Informatics |
| Depositing User: | SITI AZIZAH BINTI IDRIS - |
| Date Deposited: | 12 Mar 2020 16:33 |
| Last Modified: | 16 May 2025 12:19 |
| URI: | https://eprints.ums.edu.my/id/eprint/25233 |
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