Financial news plays a crucial role in decision-making processes across the financial sector, yet the efficient processing of this information into a structured format remains challenging. This paper presents a novel approach to financial news processing that leverages Large Language Models (LLMs) to overcome limitations that previously prevented the extraction of structured data from unstructured financial news. We introduce a system that extracts relevant company tickers from raw news article content, performs sentiment analysis at the company level, and generates summaries, all without relying on pre-structured data feeds. Our methodology combines the generative capabilities of LLMs, and recent prompting techniques, with a robust validation framework that uses a tailored string similarity approach. Evaluation on a dataset of 5530 financial news articles demonstrates the effectiveness of our approach, with 90% of articles not missing any tickers compared with current data providers, and 22% of articles having additional relevant tickers. In addition to this paper, the methodology has been implemented at scale with the resulting processed data made available through a live API endpoint, which is updated in real-time with the latest news. To the best of our knowledge, we are the first data provider to offer granular, per-company sentiment analysis from news articles, enhancing the depth of information available to market participants. We also release the evaluation dataset of 5530 processed articles as a static file, which we hope will facilitate further research leveraging financial news.

本研究旨在解决现有金融新闻处理方法难以提取结构化数据的问题。通过采用大型语言模型（LLMs）与最新的提示技术，提出了一种新颖的方法，能够从原始新闻内容中提取公司股票代码、进行公司级别的情感分析，并生成摘要。实验证明，该方法能有效提高信息提取的准确性和覆盖率，为市场参与者提供了更深入的情感分析数据，促进了相关领域的进一步研究。

从金融新闻提取结构化洞察：一种增强的大型语言模型驱动方法