{"repo":"pierpierpy/Execcomp-AI","free":true,"listed":false,"github":"https://github.com/pierpierpy/Execcomp-AI","clone":"git clone https://github.com/pierpierpy/Execcomp-AI.git","description":"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).","language":"Jupyter Notebook","stars":11,"topics":["ai","finance","nlp"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"Execcomp-AI AI-powered pipeline to extract executive compensation data from SEC DEF 14A proxy statements. 📊 Current Progress & Statistics (click to expand) 🚧 Work in Progress - Processing 100K+ SEC filings 👉 Dataset : pierjoe/execcomp-ai-sample --- Why This Project? Executive compensation data from SEC filings is valuable for academic research, corporate governance analysis, and market studies. However, extracting this information at scale is surprisingly difficult: Challenge Description ----------- ------------- Unstructured documents DEF 14A proxy statements are filed as HTML or plain text, with compensation tables embedded among hundreds of pages of legal text, footnotes, and varying formats Format changes over time SEC disclosure rules changed in 2006 — pre-2006 tables have different column names (\"Securities Underlying Options\" vs \"Option Awards\"), different structures, and often lack a Total column Tables break across pages A single Summary Compensation Table often spans multiple pages, and PDF parsers extract them as separate fragments that need to be intelligently merged Similar tables cause confusion Each proxy contains multiple compensation-related tables (director compensation, equity grants, pension benefits) that look similar to the Summary Compensation Table but contain different data No clean dataset exists Services like ExecuComp provide curated data but are expensive and limited in coverage. Raw SEC filings are free but require significant processing This ","default_branch":null,"files":null,"tree":[],"storefront":"/r/pierpierpy","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/pierpierpy/Execcomp-AI/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}