OpenEDGAR: Open Source Software for SEC EDGAR Analysis (Michael Bommarito, Daniel Martin Katz & Eric Detterman)

Our next paper — OpenEDGAR – Open Source Software for SEC Edgar Analysis is now available.  This paper explores a range of #OpenSource tools we have developed to explore the EDGAR system operated by the US Securities and Exchange Commission (SEC).  While a range of more sophisticated extraction and clause classification protocols can be developed leveraging LexNLP and other open and closed source tools, we provide some very simple code examples as an illustrative starting point.

Click here for Paper:   < SSRN > < arXiv >
Access Codebase Here: < Github >

Abstract
OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at https://github.com/LexPredict/openedgar

LexNLP: Natural Language Processing and Information Extraction For Legal and Regulatory Texts (Bommarito, Katz, Detterman)

Paper Abstract – LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like distances and dates, (iv) extract named entities such as companies and geopolitical entities, (v) transform text into features for model training, and (vi) build unsupervised and supervised models such as word embedding or tagging models. LexNLP includes pre-trained models based on thousands of unit tests drawn from real documents available from the SEC EDGAR database as well as various judicial and regulatory proceedings. LexNLP is designed for use in both academic research and industrial applications, and is distributed at https://github.com/LexPredict/lexpredict-lexnlp

LexPredict Team Releases Open Source Analytics Platform ContraxSuite – Now Live on Github!

Live on Github – the LexPredict Team has open-sourced ContraxSuite 1.0, fundamentally altering the economics of the contract and legal document analytics space – now you can perpetually own a solution with a $0 license and $0 per document fees. But we’re just getting started. Stay tuned over the next few weeks as we release even more documentation and developer examples on Github.