Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM
Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stoc...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-06-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1403.pdf |
| _version_ | 1827929410486927360 |
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| author | Jin-Xian Liu Jenq-Shiou Leu Stefan Holst |
| author_facet | Jin-Xian Liu Jenq-Shiou Leu Stefan Holst |
| author_sort | Jin-Xian Liu |
| collection | DOAJ |
| description | Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4–5% higher than other techniques. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments. |
| first_indexed | 2024-03-13T06:21:52Z |
| format | Article |
| id | doaj.art-9ace39941a7c47cf8ead4af8c76715ff |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-13T06:21:52Z |
| publishDate | 2023-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-9ace39941a7c47cf8ead4af8c76715ff2023-06-09T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922023-06-019e140310.7717/peerj-cs.1403Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVMJin-Xian Liu0Jenq-Shiou Leu1Stefan Holst2Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Computer Science and Networks, Kyushu Institute of Technology, Fukuoka Prefecture, JapanInvestor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4–5% higher than other techniques. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments.https://peerj.com/articles/cs-1403.pdfSentiment analysisStocktwitsStock price predictionMachine learningSVMSPY |
| spellingShingle | Jin-Xian Liu Jenq-Shiou Leu Stefan Holst Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM PeerJ Computer Science Sentiment analysis Stocktwits Stock price prediction Machine learning SVM SPY |
| title | Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
| title_full | Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
| title_fullStr | Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
| title_full_unstemmed | Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
| title_short | Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
| title_sort | stock price movement prediction based on stocktwits investor sentiment using finbert and ensemble svm |
| topic | Sentiment analysis Stocktwits Stock price prediction Machine learning SVM SPY |
| url | https://peerj.com/articles/cs-1403.pdf |
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