Stock price prediction using sentic API
This study investigates the potential of sentiment analysis derived from textual data across platforms like Reddit, StockTwits, Benzinga, and Twitter to enhance stock price prediction and develop trading strategies. Leveraging SenticNet for sentiment analysis, we explore the relationship between inv...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175063 |
| _version_ | 1824454279813922816 |
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| author | Phoa, Justyn Zairen |
| author2 | Erik Cambria |
| author_facet | Erik Cambria Phoa, Justyn Zairen |
| author_sort | Phoa, Justyn Zairen |
| collection | NTU |
| description | This study investigates the potential of sentiment analysis derived from textual data across platforms like Reddit, StockTwits, Benzinga, and Twitter to enhance stock price prediction and develop trading strategies. Leveraging SenticNet for sentiment analysis, we explore the relationship between investor sentiments and stock price movements. While some trading strategies show abnormal excess returns over 8 years, outperforming the market with higher Sharpe and CAGR ratios, Fama-Macbeth regressions reveal a lack of systemic alpha. We acknowledge limitations in using news headlines as sentiment proxies and suggest further research into the interplay between sentiment analysis and established financial factors to refine predictive models and understand stock price movements better. |
| first_indexed | 2025-02-19T03:19:48Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175063 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:19:48Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1750632024-04-19T15:45:25Z Stock price prediction using sentic API Phoa, Justyn Zairen Erik Cambria School of Computer Science and Engineering cambria@ntu.edu.sg Computer and Information Science NLP Sentiment analysis Fama-Macbeth regression This study investigates the potential of sentiment analysis derived from textual data across platforms like Reddit, StockTwits, Benzinga, and Twitter to enhance stock price prediction and develop trading strategies. Leveraging SenticNet for sentiment analysis, we explore the relationship between investor sentiments and stock price movements. While some trading strategies show abnormal excess returns over 8 years, outperforming the market with higher Sharpe and CAGR ratios, Fama-Macbeth regressions reveal a lack of systemic alpha. We acknowledge limitations in using news headlines as sentiment proxies and suggest further research into the interplay between sentiment analysis and established financial factors to refine predictive models and understand stock price movements better. Bachelor's degree 2024-04-19T02:28:32Z 2024-04-19T02:28:32Z 2024 Final Year Project (FYP) Phoa, J. Z. (2024). Stock price prediction using sentic API. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175063 https://hdl.handle.net/10356/175063 en SCSE23-0152 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science NLP Sentiment analysis Fama-Macbeth regression Phoa, Justyn Zairen Stock price prediction using sentic API |
| title | Stock price prediction using sentic API |
| title_full | Stock price prediction using sentic API |
| title_fullStr | Stock price prediction using sentic API |
| title_full_unstemmed | Stock price prediction using sentic API |
| title_short | Stock price prediction using sentic API |
| title_sort | stock price prediction using sentic api |
| topic | Computer and Information Science NLP Sentiment analysis Fama-Macbeth regression |
| url | https://hdl.handle.net/10356/175063 |
| work_keys_str_mv | AT phoajustynzairen stockpricepredictionusingsenticapi |