  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.
