AI-based stock price prediction
The rise of retail investors in recent years has quietly created an opportunity for anyone to evaluate the stock market via their mobile device, tablet, or desktop and potentially grow their wealth. With excessive volatility in the stock market influenced by sentiment and inflation, the art of stock...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166990 |
| _version_ | 1811695539533643776 |
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| author | Lim, Yong |
| author2 | Alex Chichung Kot |
| author_facet | Alex Chichung Kot Lim, Yong |
| author_sort | Lim, Yong |
| collection | NTU |
| description | The rise of retail investors in recent years has quietly created an opportunity for anyone to evaluate the stock market via their mobile device, tablet, or desktop and potentially grow their wealth. With excessive volatility in the stock market influenced by sentiment and inflation, the art of stock market forecasting has become a topic of global interest. This study aims to predict stock values based on the LSTM model while examining the RNN and XGBoost models. Hence, by analyzing data from Yahoo! Finance and Twitter, the study provides an in-depth examination of the performance evaluation of the three models. The results show that the LSTM and RNN models outperform the XGBoost model in predicting short-term stock volatility. |
| first_indexed | 2024-10-01T07:25:05Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166990 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:25:05Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1669902023-07-07T15:43:16Z AI-based stock price prediction Lim, Yong Alex Chichung Kot School of Electrical and Electronic Engineering EACKOT@ntu.edu.sg Engineering::Electrical and electronic engineering The rise of retail investors in recent years has quietly created an opportunity for anyone to evaluate the stock market via their mobile device, tablet, or desktop and potentially grow their wealth. With excessive volatility in the stock market influenced by sentiment and inflation, the art of stock market forecasting has become a topic of global interest. This study aims to predict stock values based on the LSTM model while examining the RNN and XGBoost models. Hence, by analyzing data from Yahoo! Finance and Twitter, the study provides an in-depth examination of the performance evaluation of the three models. The results show that the LSTM and RNN models outperform the XGBoost model in predicting short-term stock volatility. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-20T13:02:23Z 2023-05-20T13:02:23Z 2023 Final Year Project (FYP) Lim, Y. (2023). AI-based stock price prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166990 https://hdl.handle.net/10356/166990 en P3050-212 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Lim, Yong AI-based stock price prediction |
| title | AI-based stock price prediction |
| title_full | AI-based stock price prediction |
| title_fullStr | AI-based stock price prediction |
| title_full_unstemmed | AI-based stock price prediction |
| title_short | AI-based stock price prediction |
| title_sort | ai based stock price prediction |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/166990 |
| work_keys_str_mv | AT limyong aibasedstockpriceprediction |