EDGAR-CRAWLER: Extract Key Financial Data from SEC Filings Effortlessly 🚀

EDGAR-CRAWLER simplifies access to financial text data by downloading SEC EDGAR filings and transforming these complex, unstructured documents into structured, standardized JSON files, making it easier to use them for downstream NLP tasks and financial analysis.

EDGAR-CRAWLER has 2 core functionalities:

• 📥 Seamless downloading: Retrieve and download financial filings from all US publicly-traded companies based on your specified filters, like year, quarters, filing type, etc.
• 🔍 Structured output: Extract and parse key sections from 10-K, 10-Q, and 8-K filings into a nice-and-easy standardized JSON format. (filings supported: 10-K, 10-Q, 8-K)

🚨 News

• 2024-10-14: We added support for JSON parsing of 10-Q filings. (@Bailefan)
• 2024-10-05: We added support for JSON parsing of 8-K filings. (@Bailefan)
• 2023-12-06: We had a Lightning Talk about EDGAR-CRAWLER at the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS), hosted at EMNLP 2023, in Singapore.
• 2023-01-16: EDGAR-CORPUS, the biggest financial NLP corpus (generated from EDGAR-CRAWLER), is available as a HuggingFace 🤗 dataset card. See Accompanying Resources for more details.
• 2022-10-13: Updated documentation and fixed a minor import bug.
• 2022-04-03: EDGAR-CRAWLER is available for Windows systems too.
• 2021-11-11: We presented EDGAR-CORPUS, our sister work that started it all, at ECONLP 2021 (EMNLP Workshop) at the Dominican Republic. See Accompanying Resources for more details.

Table of Contents

• Example Outputs
• Install
• Usage
• Citation
• Accompanying Resources
• Contributing
• License

Example Outputs

EDGAR-CRAWLER produces structured JSON outputs for easy handling of unstructured/complex SEC/EDGAR filings. Below are examples of these clean, extracted outputs for each supported filing type:

10-K filing (Annual Report)

Original report: Apple 10-K from 2022

  {
    "cik": "320193",
    "company": "Apple Inc.",
    "filing_type": "10-K",
    "filing_date": "2022-10-28",
    "period_of_report": "2022-09-24",
    "sic": "3571",
    "state_of_inc": "CA",
    "state_location": "CA",
    "fiscal_year_end": "0924",
    "filing_html_index": "https://www.sec.gov/Archives/edgar/data/320193/0000320193-22-000108-index.html",
    "htm_filing_link": "https://www.sec.gov/Archives/edgar/data/320193/000032019322000108/aapl-20220924.htm",
    "complete_text_filing_link": "https://www.sec.gov/Archives/edgar/data/320193/0000320193-22-000108.txt",
    "filename": "320193_10K_2022_0000320193-22-000108.htm",
    "item_1": "Item 1. Business\nCompany Background\nThe Company designs, manufactures ...",
    "item_1A": "Item 1A. Risk Factors\nThe Company’s business, reputation, results of ...",
    "item_1B": "Item 1B. Unresolved Staff Comments\nNone.",
    "item_1C": "",
    "item_2": "Item 2. Properties\nThe Company’s headquarters are located in Cupertino, California. ...",
    "item_3": "Item 3. Legal Proceedings\nEpic Games\nEpic Games, Inc. (“Epic”) filed a lawsuit ...",
    "item_4": "Item 4. Mine Safety Disclosures\nNot applicable. ...",
    "item_5": "Item 5. Market for Registrant’s Common Equity, Related Stockholder ...",
    "item_6": "Item 6. [Reserved]\nApple Inc. | 2022 Form 10-K | 19",
    "item_7": "Item 7. Management’s Discussion and Analysis of Financial Condition ...",
    "item_8": "Item 8. Financial Statements and Supplementary Data\nAll financial ...",
    "item_9": "Item 9. Changes in and Disagreements with Accountants on Accounting and Financial Disclosure\nNone.",
    "item_9A": "Item 9A. Controls and Procedures\nEvaluation of Disclosure Controls and ...",
    "item_9B": "Item 9B. Other Information\nRule 10b5-1 Trading Plans\nDuring the three months ...",
    "item_9C": "Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections\nNot applicable. ...",
    "item_10": "Item 10. Directors, Executive Officers and Corporate Governance\nThe information required ...",
    "item_11": "Item 11. Executive Compensation\nThe information required by this Item will be included ...",
    "item_12": "Item 12. Security Ownership of Certain Beneficial Owners and Management and ...",
    "item_13": "Item 13. Certain Relationships and Related Transactions, and Director Independence ...",
    "item_14": "Item 14. Principal Accountant Fees and Services\nThe information required ...",
    "item_15": "Item 15. Exhibit and Financial Statement Schedules\n(a)Documents filed as part ...",
    "item_16": "Item 16. Form 10-K Summary\nNone.\nApple Inc. | 2022 Form 10-K | 57"
  }

10-Q (Quarterly Report)

8-K (Important Current Report)

Install

• Download EDGAR-CRAWLER locally via SSH or HTTPS:

  
  # Method 1: SSH 
  git clone https://github.com/nlpaueb/edgar-crawler.git 

Method 2: HTTPS

git clone git@github.com:nlpaueb/edgar-crawler.git

- Then, it's recommended to create a new virtual environment using Python 3.8 by [installing and using Anaconda](https://docs.anaconda.com/anaconda/install/index.html).
```bash
conda create -n edgar-crawler-venv python=3.8 # After installing Anaconda, create a venv with python 3.8+
conda activate edgar-crawler-venv # Activate the environment

• Then, install the toolkit's dependencies via:

  pip install -r requirements.txt # Install requirements for edgar-crawler

Usage

• Before running any script, you should edit the config.json file, which configures the behavior of our 2 modules (one for downloading the filings of your choice, the other one for getting the structured output of them).

  • Arguments for edgar_crawler.py, the module to download financial reports:
    • start_year XXXX: the year range to start from (default is 2023).
    • end_year YYYY: the year range to end to (default is 2023).
    • quarters: the quarters that you want to download filings from (List).
      Default value is: [1, 2, 3, 4].
    • filing_types: list of filing types to download.
      Default value is: ['10-K', '8-K', '10-Q'].
    • cik_tickers: list or path of file containing CIKs or Tickers. e.g. [789019, "1018724", "AAPL", "TWTR"]
      In case of file, provide each CIK or Ticker in a different line.
      If this argument is not provided, then the toolkit will download annual reports for all the U.S. publicly traded companies.
    • user_agent: the User-agent (name/email) that will be declared to SEC EDGAR.
    • raw_filings_folder: the name of the folder where downloaded filings will be stored.
      Default value is 'RAW_FILINGS'.
    • indices_folder: the name of the folder where EDGAR TSV files will be stored. These are used to locate the annual reports. Default value is 'INDICES'.
    • filings_metadata_file: CSV filename to save metadata from the reports.
    • skip_present_indices: Whether to skip already downloaded EDGAR indices or download them nonetheless.
      Default value is True.
  • Arguments for extract_items.py, the module to clean and extract textual data from already-downloaded reports:
  • raw_filings_folder: the name of the folder where the downloaded documents are stored.
    Default value s 'RAW_FILINGS'.
  • extracted_filings_folder: the name of the folder where extracted documents will be stored.
    Default value is 'EXTRACTED_FILINGS'.
    For each downloaded report, a corresponding JSON file will be created containing the item sections as key-pair values.
  • filings_metadata_file: CSV filename to load reports metadata (Provide the same csv file as in edgar_crawler.py).
  • filing_types: list of filing types to extract.
  • include_signature: Whether to include the signature section after the last item or not.
  • items_to_extract: a list with the certain item sections to extract.
    e.g. ['7','8'] to extract 'Management’s Discussion and Analysis' and 'Financial Statements' section items for 10-K reports.
    By default, this list is empty, in which case all items are extracted.
  • remove_tables: Whether to remove tables containing mostly numerical (financial) data. This work is mostly to facilitate NLP research where, often, numerical tables are not useful.
  • skip_extracted_filings: Whether to skip already extracted filings or extract them nonetheless.
    Default value is True.

• To download financial reports from EDGAR, run python edgar_crawler.py.

• To clean and extract specific item sections from already-downloaded documents, run python extract_items.py.

  • Reminder: We currently support the structured output for 10-K, 10-Q and 8-K documents.
  • Note: For older 10-Q filings, it might not be possible to extract any items for specific parts (Part 1 or Part 2). Because of this, we also include each full part in the output file as a separate entry.

Citation

An EDGAR-CRAWLER paper is on its way. Until then, please cite the relevant EDGAR-CORPUS paper published at the 3rd Economics and Natural Language Processing (ECONLP) workshop at EMNLP 2021 (Punta Cana, Dominican Republic):

@inproceedings{loukas-etal-2021-edgar-corpus-and-edgar-crawler,
    title = "{EDGAR}-{CORPUS}: {B}illions of {T}okens {M}ake {T}he {W}orld {G}o {R}ound",
    author = "Loukas, Lefteris  and
      Fergadiotis, Manos  and
      Androutsopoulos, Ion  and
      Malakasiotis, Prodromos",
    booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing (ECONLP)",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.econlp-1.2",
    pages = "13--18",
}

Read the EDGAR-CORPUS paper here: https://aclanthology.org/2021.econlp-1.2/

Star History

Accompanying Resources

Here are some additional resources related to EDGAR-CRAWLER:

• EDGAR-CORPUS on HuggingFace: The largest corpus for financial NLP research, built from EDGAR-CRAWLER. Available at 🤗 datasets.

  • EDGAR-CORPUS on HuggingFace 🤗

• EDGAR-CORPUS on Zenodo: The same corpus is also available on Zenodo.

  • EDGAR-CORPUS on Zenodo

• Financial Word2Vec Embeddings: Word2Vec embeddings trained on EDGAR-CORPUS.

  • EDGAR-W2V on Zenodo

Contributing

PRs and contributions are accepted.

Please use the Feature Branch Workflow.

Issues

Please create an issue on GitHub instead of emailing us directly so all possible users can benefit from the troubleshooting.

License

This software is licensed under the GNU General Public License v3.0, a license approved by the Open-Source Initiative (OSI).