用新闻传播范围和上下文增强大模型，提升股票短期走势预测准确率。
FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs
- 融合新闻传播广度与具体上下文，优化提示词设计。
- 相较现有方法，股票走势预测准确率提升8%。
- 适合金融量化、NLP应用研究者参考。
金融情绪分析对理解新闻对股价的影响至关重要。近年来，大型语言模型（LLMs）因其强大的文本分析能力被广泛用于该任务。然而，这些模型通常仅关注新闻内容本身，忽略其传播情况，限制了对短期股价变动的准确预测。此外，现有方法常缺乏充分的上下文数据和明确的提示指令，削弱了LLM的解读能力。本文提出一种数据驱动的方法，通过引入新闻传播范围、上下文数据和明确指令，增强基于情感的股票走势预测。我们对近期公司相关新闻进行聚类，评估其传播广度与影响力，并将更具体的事实信息与精准指令融入提示中。基于此构建指令微调数据集，对LLM进行训练以预测短期股价变动。实验结果表明，本方法相比现有方法在预测准确率上提升8%。
原文摘要 · Abstract (English)
Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its dissemination, which hampers accurate prediction of short-term stock movements. Additionally, current methods often lack sufficient contextual data and explicit instructions in their prompts, limiting LLMs' ability to interpret news. In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. This data is used to construct an instruction tuning dataset to fine-tune an LLM for predicting short-term stock price movements. Our experimental results show that our approach improves prediction accuracy by 8\% compared to existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。