Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
- URL: http://arxiv.org/abs/2407.15788v1
- Date: Mon, 22 Jul 2024 16:47:31 GMT
- Title: Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
- Authors: Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike,
- Abstract summary: This paper presents a novel approach to financial news processing that leverages Large Language Models (LLMs)
We introduce a system that extracts relevant company tickers from raw news article content, performs sentiment analysis at the company level, and generates summaries.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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