دورية أكاديمية

Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM

التفاصيل البيبلوغرافية
العنوان: Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM
المؤلفون: Liu, Jin-Xian, Leu, Jenq-Shiou, Holst, Stefan
المساهمون: The Kyushu Institute of Technology—National Taiwan University of Science and Technology Joint Research Program
المصدر: PeerJ Computer Science ; volume 9, page e1403 ; ISSN 2376-5992
بيانات النشر: PeerJ
سنة النشر: 2023
المجموعة: PeerJ (E-Journal - via CrossRef)
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
اللغة: English
DOI: 10.7717/peerj-cs.1403
الإتاحة: https://doi.org/10.7717/peerj-cs.1403Test
https://peerj.com/articles/cs-1403.pdfTest
https://peerj.com/articles/cs-1403.xmlTest
https://peerj.com/articles/cs-1403.htmlTest
حقوق: https://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.DF931155
قاعدة البيانات: BASE