Cargando…
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...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280432/ https://www.ncbi.nlm.nih.gov/pubmed/37346695 http://dx.doi.org/10.7717/peerj-cs.1403 |
_version_ | 1785060792830263296 |
---|---|
author | Liu, Jin-Xian Leu, Jenq-Shiou Holst, Stefan |
author_facet | Liu, Jin-Xian Leu, Jenq-Shiou Holst, Stefan |
author_sort | Liu, Jin-Xian |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-10280432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804322023-06-21 Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM Liu, Jin-Xian Leu, Jenq-Shiou Holst, Stefan PeerJ Comput Sci Artificial Intelligence 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. PeerJ Inc. 2023-06-07 /pmc/articles/PMC10280432/ /pubmed/37346695 http://dx.doi.org/10.7717/peerj-cs.1403 Text en ©2023 Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Liu, Jin-Xian Leu, Jenq-Shiou Holst, Stefan Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM |
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 | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280432/ https://www.ncbi.nlm.nih.gov/pubmed/37346695 http://dx.doi.org/10.7717/peerj-cs.1403 |
work_keys_str_mv | AT liujinxian stockpricemovementpredictionbasedonstocktwitsinvestorsentimentusingfinbertandensemblesvm AT leujenqshiou stockpricemovementpredictionbasedonstocktwitsinvestorsentimentusingfinbertandensemblesvm AT holststefan stockpricemovementpredictionbasedonstocktwitsinvestorsentimentusingfinbertandensemblesvm |