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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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Autores principales: Liu, Jin-Xian, Leu, Jenq-Shiou, Holst, Stefan
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
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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.
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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
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