pierpierpy/Execcomp-AI
VLM pipeline to extract executive compensation data from SEC DEF 14A proxy statements. Uses VLM for table classification and LLM for structured data extraction from 100K+ filings (2005-2022).
This project helps financial researchers, governance analysts, and market strategists automatically extract executive compensation data from SEC DEF 14A proxy statements. It takes raw SEC filings and produces structured JSON output containing compensation details like salary, bonus, and stock awards for executives. This is for professionals who need to analyze executive pay at scale, without the high cost of commercial data providers.
Use this if you need to systematically gather and analyze executive compensation details from a large volume of SEC proxy statements for research or compliance purposes.
Not ideal if you only need compensation data for a few specific companies or are looking for real-time extraction rather than a bulk historical processing tool.
Stars
10
Forks
—
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Feb 04, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/pierpierpy/Execcomp-AI"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Shubxam/Nifty-500-Live-Sentiment-Analysis
Live Sentiment Analysis dashboard of NIFTY 500 universe of stocks using plotly and streamlit
lefterisloukas/edgar-crawler
The only open-source toolkit that can download SEC EDGAR financial reports and extract textual...
yya518/FinBERT
A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
shirosaidev/stocksight
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python...
louisowen6/SENN
Code implementation of "SENN: Stock Ensemble-based Neural Network for Stock Market Prediction...