AI-based stock market trending analysis
Stock market prediction is gaining popularity and is widely used due to the lucrative rewards it reap. With accurate prediction of stock prices, we are able to yield significant monetary profits. Stock prices are essentially determined by its demand and supply at that point of time in the stock mark...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156512 |
| _version_ | 1824456781929119744 |
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| author | Chin, Yi Xing |
| author2 | Li Fang |
| author_facet | Li Fang Chin, Yi Xing |
| author_sort | Chin, Yi Xing |
| collection | NTU |
| description | Stock market prediction is gaining popularity and is widely used due to the lucrative rewards it reap. With accurate prediction of stock prices, we are able to yield significant monetary profits. Stock prices are essentially determined by its demand and supply at that point of time in the stock market. The factors affecting the stock’s demand and supply can be primarily grouped into Technical and Sentimental Indicators. With the advancement in the field of Artificial Intelligence and the vast availability of data, we are now able to predict the stock market more efficiently. This project focuses on finding the best machine learning model to predict stock prices. Currently, the LSTM model and SVM display one of the highest accuracy in predicting stock prices with the technical indicators using time series models. While the VADER and TextBlob models have shown high accuracy in predicting stock prices with sentimental indicators using sentiment analysis. The proposed methodology then inputs results from the best sentiment analysis model as variable together with the stock’s price information into the LSTM model to further enhance prediction capabilities. |
| first_indexed | 2025-02-19T03:59:34Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156512 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:59:34Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1565122022-04-19T05:58:19Z AI-based stock market trending analysis Chin, Yi Xing Li Fang School of Computer Science and Engineering ASFLi@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Stock market prediction is gaining popularity and is widely used due to the lucrative rewards it reap. With accurate prediction of stock prices, we are able to yield significant monetary profits. Stock prices are essentially determined by its demand and supply at that point of time in the stock market. The factors affecting the stock’s demand and supply can be primarily grouped into Technical and Sentimental Indicators. With the advancement in the field of Artificial Intelligence and the vast availability of data, we are now able to predict the stock market more efficiently. This project focuses on finding the best machine learning model to predict stock prices. Currently, the LSTM model and SVM display one of the highest accuracy in predicting stock prices with the technical indicators using time series models. While the VADER and TextBlob models have shown high accuracy in predicting stock prices with sentimental indicators using sentiment analysis. The proposed methodology then inputs results from the best sentiment analysis model as variable together with the stock’s price information into the LSTM model to further enhance prediction capabilities. Bachelor of Engineering (Computer Engineering) 2022-04-19T05:58:19Z 2022-04-19T05:58:19Z 2022 Final Year Project (FYP) Chin, Y. X. (2022). AI-based stock market trending analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156512 https://hdl.handle.net/10356/156512 en application/pdf application/octet-stream Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Chin, Yi Xing AI-based stock market trending analysis |
| title | AI-based stock market trending analysis |
| title_full | AI-based stock market trending analysis |
| title_fullStr | AI-based stock market trending analysis |
| title_full_unstemmed | AI-based stock market trending analysis |
| title_short | AI-based stock market trending analysis |
| title_sort | ai based stock market trending analysis |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/156512 |
| work_keys_str_mv | AT chinyixing aibasedstockmarkettrendinganalysis |