App for predicting stock price fluctuation with neural network
Prediction of stock price fluctuations with the use of Neural Network, mainly the LSTM Model. Datasets from the SPY Index Fund is used to train the LSTM Model with cleaning of the data. The data is separated into individual days of the week to be trained into the LSTM Model to predict each day of th...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/167779 |
| _version_ | 1826112025436094464 |
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| author | Yeo, James Gui Zhong |
| author2 | Wong Liang Jie |
| author_facet | Wong Liang Jie Yeo, James Gui Zhong |
| author_sort | Yeo, James Gui Zhong |
| collection | NTU |
| description | Prediction of stock price fluctuations with the use of Neural Network, mainly the LSTM Model. Datasets from the SPY Index Fund is used to train the LSTM Model with cleaning of the data. The data is separated into individual days of the week to be trained into the LSTM Model to predict each day of the week. This method of parsing dataset to the days of the week yield promising results, which is then translated and seen from the application made after. Using the model, the application will also be able to trade automatically with the backend system in place. |
| first_indexed | 2024-10-01T03:00:30Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167779 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:00:30Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1677792023-07-07T15:43:01Z App for predicting stock price fluctuation with neural network Yeo, James Gui Zhong Wong Liang Jie School of Electrical and Electronic Engineering liangjie.wong@ntu.edu.sg Engineering::Electrical and electronic engineering Prediction of stock price fluctuations with the use of Neural Network, mainly the LSTM Model. Datasets from the SPY Index Fund is used to train the LSTM Model with cleaning of the data. The data is separated into individual days of the week to be trained into the LSTM Model to predict each day of the week. This method of parsing dataset to the days of the week yield promising results, which is then translated and seen from the application made after. Using the model, the application will also be able to trade automatically with the backend system in place. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-06-05T00:11:49Z 2023-06-05T00:11:49Z 2023 Final Year Project (FYP) Yeo, J. G. Z. (2023). App for predicting stock price fluctuation with neural network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167779 https://hdl.handle.net/10356/167779 en A2041-221 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Yeo, James Gui Zhong App for predicting stock price fluctuation with neural network |
| title | App for predicting stock price fluctuation with neural network |
| title_full | App for predicting stock price fluctuation with neural network |
| title_fullStr | App for predicting stock price fluctuation with neural network |
| title_full_unstemmed | App for predicting stock price fluctuation with neural network |
| title_short | App for predicting stock price fluctuation with neural network |
| title_sort | app for predicting stock price fluctuation with neural network |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167779 |
| work_keys_str_mv | AT yeojamesguizhong appforpredictingstockpricefluctuationwithneuralnetwork |