基于多式联网架构的库存时间预测的改进:利用大型语言模型 (LLM) 来提高文本质量
Mingming Chen1,2, Yifan Tang1, Qi Qi1
1Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.
PloS one
|June 18, 2025
概括
像GPT-4这样的大型语言模型 (LLM) 通过过在线投资者评论来改善股票时间预测. 综合分析的评论与财务数据的多式联络方法可以提高预测的准确性.
科学领域:
- 金融技术 金融技术
- 自然语言处理自然语言处理.
- 计算金融是指计算金融.
背景情况:
- 在线投资者情绪分析面临着数据质量,冗余性和真实性的挑战.
- 传统的定量方法往往忽略了来自社交媒体的定性见解.
- 准确的股票时间预测对于投资决策至关重要.
研究的目的:
- 通过使用大型语言模型 (LLM) 来分析在线投资者评论来增强股票时间预测.
- 开发和评估一个多式联网架构,将LLM处理的情绪与财务数据集成在一起.
- 评估GPT-4在过和分析非结构化财务评论方面的有效性.
主要方法:
- 利用GPT-4从中国银行数据中过和分析投资者评论.
- 开发了一个多式联网架构,将过的评论数据与股票价格和技术指标相结合.
- 将GPT-4过与四个基线模型进行比较,并使用财务指标评估业绩.
主要成果:
- GPT-4显著改善了关键的财务指标,包括利亏损比率,胜利率和超额回报率.
- 拟议的多式联运架构在库存时间预测方面表现优于基线模型.
- 通过LLM对评论数据的有效预处理,提高了与定量财务信息的整合.
结论:
- 大型语言模型,特别是GPT-4,为提高财务预测准确性提供了强大的工具.
- 多式联网架构为整合定性情绪数据与定量金融分析提供了强大的框架.
- 该方法表明,在各种金融市场中具有更广泛的应用潜力,有助于投资者决策支持.
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