Summary of A Scalable Data-driven Framework For Systematic Analysis Of Sec 10-k Filings Using Large Language Models, by Syed Affan Daimi et al.
A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models
by Syed Affan Daimi, Asma Iqbal
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel approach leverages large language models (LLMs) to systematically analyze and rate the performance of companies based on their SEC 10-K filings, providing a fast, cost-effective, and comprehensive method for evaluating corporate health. The proposed system accurately identifies and segments required sections in 10-K filings, isolating key textual content that contains critical information about the company. Cohere’s Command-R+ LLM is used to generate quantitative ratings across various performance metrics, which are then processed and visualized to provide actionable insights. This no-code solution for running the data pipeline and creating visualizations showcases rating results and provides year-on-year comparisons of company performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps companies listed on the NYSE by giving them a way to easily see how well they’re doing compared to others. It uses special computer models to look at what companies are saying in their annual reports, or 10-K filings, and gives them a score based on things like financial health, environmental sustainability, innovation, and workforce management. The model can even show year-on-year comparisons to help businesses see how they’re improving over time. |
