Predictive Analytics: Bursa Malaysia Stocks price prediction
Forecasting the stock market with deep neural networks is a trend nowadays. However, the results are very different between different models as well as within the same model with different architecture. Therefore, a careful research should be done on choosing type of model and selecting the arc...
| Main Author: | Kariom Lang-rot, Bull Mawat |
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| Format: | Final Year Project |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.21759 / |
| Published: |
IRC
2020
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/21759/1/23097_Bull%20Mawat%20Kariom.pdf http://utpedia.utp.edu.my/21759/ |
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| Summary: |
Forecasting the stock market with deep neural networks is a trend nowadays.
However, the results are very different between different models as well as within
the same model with different architecture. Therefore, a careful research should be
done on choosing type of model and selecting the architecture of the model. Also,
hyper-parameters should be properly selected based on type of data and a nature of
the problem. A lot of researches have been done on Bursa Malaysia stock market and
different algorithms have been tests in the past. Therefore, this paper objective is to
use the latest deep learning time series model known as long-short term memory to
forecast the stock prices of the Malaysian largest stock market. |
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