基于StockTwits投资者情绪的股票价格走势预测,使用FinBERT和SVM集团进行预测
Jin-Xian Liu1, Jenq-Shiou Leu1, Stefan Holst2
1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
PeerJ. Computer science
|June 22, 2023
概括
这项研究通过使用FinBERT进行社交媒体情绪分析和整体支持向量机来增强股票价格预测. 这种方法提高了预测交易所交易基金流动的准确性.
科学领域:
- * 计算金融学
- * 自然语言处理 (Natural Language Processing) 是一种语言处理系统.
- * 机器学习 * 机器学习
背景情况:
- * 投资者情绪显著影响股票市场动态.
- *以前的研究利用社交媒体和新闻进行情绪分析来预测股价.
- * 从社交媒体,特别是中立评论中准确地捕捉投资者的情绪存在挑战.
研究的目的:
- * 调查来自社交媒体,特别是StockTwits的投资者情绪对预测股价走势的有效性.
- * 解决现有的情绪分析工具和数据可用性的局限性.
- * 提出和评估一个改进的预测模型,结合先进的情绪分析.
主要方法:
- * 采用了FinBERT,这是一个预先训练的语言模型,用于分析财务文本情绪.
- * 开发了一个集体支向量机 (SVM) 来提高预测准确度.
- * 利用滚动窗口方法预测SPDR S&P 500指数交易所交易基金的走势,以避免前偏差.
主要成果:
- *FinBERT表现出优越的情绪分析性能,与其他技术相比,F1得分高出4 - 5%.
- * 拟议的整体SVM模型显著提高了比标准SVM更准确的股票价格运动预测准确性.
- * 该研究证实了FinBERT在为金融市场创造情绪方面的有效性.
结论:
- *FinBERT是分析金融社交媒体投资者情绪的高效工具.
- *集成机器学习模型,结合先进的情绪分析,提供优越的股票市场预测能力.
- *这些发现为在定量金融中利用社交媒体数据提供了强有力的框架.
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