The goal of this project is to download large numbers of company filings from the SEC EDGAR service, extract key text sections of interest, and store them in an easily accessible and readable format.
Download key text sections of SEC EDGAR company filings. Format, organise and store the text excerpts ready for both automated processing (NLP) and for human reading (spot-checking). Structured storage of text and metadata, with logging of failed document analyses. Suitable for automation of large-scale downloads, with flexibility to customise which sections of the documents are extracted. Compatible with all main EDGAR document formats from 1993 onwards, and easily adapted or extended to extract different sections of interest in EDGAR filings.
Generally accurate in extracting text, but lots of room for improvement. Comments and contributions welcome!
- I completed this project during a sabbatical from my job.
- I used SEC-Edgar-Crawler for initial ideas which helped this project.
- Thanks to my colleagues at Rosenberg Equities for help with an earlier attempt to download EDGAR data.
Clone the repo, and install the packages in requirements.txt.
Typical usage. This will download the 500 large US companies in the included 'companies_list.txt' file. Run from the project folder.
python secedgartext --storage=/tmp/my_storage_folder
Download of a full history of key sections from 10-K and 10-Q filings for (most) US companies takes less than 40GB storage: around 1 million text excerpt files, plus a similar number of metadata files.
History and future of EDGAR, links to key SEC procedures documents etc. Electronic filing was mandatory from 1996.
Other packages
Lots of open source projects have automated access to EDGAR filings. A few access text data, like this one. Most focus on downloading whole filing documents, financial statements information, or parsing XBRL filings. This package aims to make access to large volumes of text information easier and more consistent.
https://github.com/datasets/edgar A nice introduction to the EDGAR database website. https://github.com/eliangcs/pystock-crawler A project which also accesses daily stock prices from Yahoo Finance
https://github.com/rahulrrixe/sec-edgar Download a list of filings and save the complete document locally. https://github.com/lukerosiak/pysec Django code for parsing XBRL documents. https://github.com/eliangcs/pystock-crawler A project which also accesses daily stock prices from Yahoo Finance
Academic research
Professor Bill McDonald, with collaborators including Prof Tim Loughran has led much of the academic reserach into company filings' text data in recent years. He much shares the approach that he used for extracting EDGAR filings text data on his website. The approach to scraping the text data is somewhat different to that used in this project, but it has a similar goal, and the documentation gives a great introduction to the structure of the HTML filing documents. The website includes links to related research, and plenty of guidance on doing downstream research on EDGAR text documents.