Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
<|reference_start|>Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach: 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.<|reference_end|>
@article{dolphin2024extracting,
    title={Extracting Structured Insights from Financial News: An Augmented LLM
  Driven Approach},
    author={Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie
  Adams, Quinton Pike},
    journal={arXiv preprint arXiv:2407.15788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15788},
    primaryClass={cs.CL}
}