Prediction of stock market using artificial intelligence and sentiment analysis
This paper delves into enhancing stock price prediction using Artificial Intelligence (AI) and Machine Learning (ML) techniques, given the stock market's unpredictable and dynamic nature. Three ML models, namely Decision Tree (DT), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)...
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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/10356/176454 |
| _version_ | 1811686653801005056 |
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| author | Lee, Shanice Shi Ying |
| author2 | Mohammed Yakoob Siyal |
| author_facet | Mohammed Yakoob Siyal Lee, Shanice Shi Ying |
| author_sort | Lee, Shanice Shi Ying |
| collection | NTU |
| description | This paper delves into enhancing stock price prediction using Artificial Intelligence (AI) and Machine Learning (ML) techniques, given the stock market's unpredictable and dynamic nature. Three ML models, namely Decision Tree (DT), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), were employed to predict Tesla's stock prices and its trends. Sentiment Analysis using TextBlob model was also integrated to leverage its accuracy in analyzing sentiments from textual data using Elon Musk's tweets. By incorporating Sentiment Analysis into the LSTM model, nuanced market sentiments can be captured which improves the model's ability to detect sentiment-driven trends in stock prices. The predictive accuracy of the models was assessed using performance metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). |
| first_indexed | 2024-10-01T05:03:51Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176454 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:03:51Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1764542024-05-17T15:43:32Z Prediction of stock market using artificial intelligence and sentiment analysis Lee, Shanice Shi Ying Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Engineering Stock market This paper delves into enhancing stock price prediction using Artificial Intelligence (AI) and Machine Learning (ML) techniques, given the stock market's unpredictable and dynamic nature. Three ML models, namely Decision Tree (DT), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), were employed to predict Tesla's stock prices and its trends. Sentiment Analysis using TextBlob model was also integrated to leverage its accuracy in analyzing sentiments from textual data using Elon Musk's tweets. By incorporating Sentiment Analysis into the LSTM model, nuanced market sentiments can be captured which improves the model's ability to detect sentiment-driven trends in stock prices. The predictive accuracy of the models was assessed using performance metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). Bachelor's degree 2024-05-16T13:52:12Z 2024-05-16T13:52:12Z 2024 Final Year Project (FYP) Lee, S. S. Y. (2024). Prediction of stock market using artificial intelligence and sentiment analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176454 https://hdl.handle.net/10356/176454 en A3121-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Stock market Lee, Shanice Shi Ying Prediction of stock market using artificial intelligence and sentiment analysis |
| title | Prediction of stock market using artificial intelligence and sentiment analysis |
| title_full | Prediction of stock market using artificial intelligence and sentiment analysis |
| title_fullStr | Prediction of stock market using artificial intelligence and sentiment analysis |
| title_full_unstemmed | Prediction of stock market using artificial intelligence and sentiment analysis |
| title_short | Prediction of stock market using artificial intelligence and sentiment analysis |
| title_sort | prediction of stock market using artificial intelligence and sentiment analysis |
| topic | Engineering Stock market |
| url | https://hdl.handle.net/10356/176454 |
| work_keys_str_mv | AT leeshaniceshiying predictionofstockmarketusingartificialintelligenceandsentimentanalysis |