通过微调大型语言模型和归因图神经网络来探索金融情绪分析
Zongshen Mu1, Yujie Wan2, Yueting Zhuang3
1School of Big Data and Software Engineering, Chongqing University, Chongqing, China; Southwest Securities Co., Ltd, Chongqing, China.
概括
本研究引入了一个新的框架,将LLM和GNN结合起来进行金融情绪分析,通过考虑跨资产影响来改善股票预测. 该模型在中国A股市场上实现了夏普比率50%的提高.
科学领域:
- 计算金融是指计算金融.
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 金融情绪分析 (FSA) 面临的挑战是预先训练有素的LLM由于领域的特异性和数据模式的适应.
- 现有的LLM经常忽视跨资产影响,仅专注于价格预测的个人股票信息.
研究的目的:
- 开发一种新的框架,将LLM和GNN结合起来,以利用金融新闻情绪来改进股价动态建模.
- 提高LLM对金融情绪的敏感性,并有效地模拟跨资产依赖.
主要方法:
- 使用了Llama-3-8B模型,对金融情绪敏感性进行了SFT和DPO的微调.
- 开发了一个GNN来处理LLM情绪输出,创建文本属性图表来建模跨资产依赖和时间变化的相关性.
主要成果:
- 证明金融情绪显著影响中国A股市场的股价波动.
- 拟议的框架表现优于现有基线,平均夏普比率提高了50%.
结论:
- 集成的LLM-GNN框架有效地捕捉了金融情绪和跨资产动态,用于股票价格预测.
- 这种方法在应用NLP和基于图形的方法来分析金融市场方面取得了重大进展.
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