A CIMB stock price prediction case study with feedforward neural network and recurrent neural network
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 Neur...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Teknologi Malaysia Melaka (UTeM)
2018
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/25233/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/25233/ |
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| Summary: | 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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