MichaelDG/esg-commitment-verifiability

Raw SEC Item 1A Risk Factors Text

Dataset Summary

This dataset is an English-language corpus of Item 1A Risk Factors sections from SEC Form 10-K annual filings. Each row represents one curated company-year filing. The corpus was created for research on temporal changes in corporate risk disclosure, with particular interest in environmental risk language.

The dataset contains raw disclosure text only. It does not include environmental enforcement records, annotations, model outputs, labels or scores.

Supported Tasks

This is a document corpus, not a supervised benchmark. It can support document representation learning, retrieval over risk-factor disclosures, topic modeling, lexical analysis and temporal analysis of corporate risk language.

Languages

The text is English (en) and comes from U.S. SEC filings.

What Item 1A Means

SEC Form 10-K is the annual report that many U.S. public companies file with the U.S. Securities and Exchange Commission. Item 1A is the Risk Factors section of that filing. In this section, companies describe material uncertainties that could affect their business, financial condition or future results.

A risk factor is company-written disclosure about something that could go wrong. It can cover regulation, litigation, commodity prices, climate exposure, operations, supply chains, financing, cybersecurity, competition or broader macroeconomic conditions. The text is useful for studying how firms describe risk over time, but it is not evidence that a risk has already happened.

Dataset Structure

Data Instances

Each example contains a document identifier and the extracted Item 1A text.

{
  "file_name": "AEE_2019.txt",
  "text": "<full Item 1A Risk Factors text>"
}

Data Fields

The default config contains a two-column Parquet corpus:

Field	Type	Description
file_name	string	Source raw file name in TICKER_YEAR.txt format.
text	string	Full raw Item 1A Risk Factors text.

Data Splits

Split	Rows
train	1,088

The dataset has one split because it is a raw document corpus rather than a train, validation and test benchmark.

Dataset Statistics

Field	Value
Raw text rows	1,088
Companies identified from file names	138
Fiscal years identified from file names	2015-2024
Total raw words	10,482,592
Mean raw words per row	9,635
Total raw characters	71,329,285

Dataset Creation

Source Data

Source	Role in this dataset
SEC EDGAR Form 10-K filings	Original public annual reports filed by U.S. public companies.
Item 1A Risk Factors	The extracted section that contains company-written descriptions of material business risks.
edgartools	Retrieval and extraction helper used to access 10-K filings and resolve their Item 1A sections.

The original language producers are public companies filing annual reports with the U.S. Securities and Exchange Commission. The source records are public Form 10-K filings available through SEC EDGAR.

Curation Rationale

The corpus was built to support analysis of how corporate risk-factor language changes over time. Keeping the release at the raw Item 1A document level makes the source text usable for different NLP tasks without imposing predefined labels or scores.

Collection And Processing

Form 10-K annual filings were retrieved from SEC EDGAR. Item 1A sections were resolved with edgartools, then retained as document-level observations when the extracted risk-factor section was usable. The released text is not whitespace-normalized, sentence-split, keyword filtered, labeled or scored. Document identifiers preserve the TICKER_YEAR.txt naming convention used during extraction.

Annotations

The dataset contains no human or machine annotations.

Loading

from datasets import load_dataset

docs = load_dataset("MichaelDG/esg-commitment-verifiability", split="train")
first_doc = docs[0]
print(first_doc["file_name"])
print(first_doc["text"][:500])

Intended Use And Limitations

Use this dataset for NLP research on SEC risk-factor language. It can support document representation learning, retrieval, topic modeling and temporal text analysis.

The corpus is not representative of all public companies or all SEC filings. It covers curated Item 1A documents from identified company-years in the 2015-2024 fiscal-year window. Item 1A is broader than ESG disclosure. Environmental language may be absent from some documents.

Risk-factor text is company-written disclosure about possible risks. It should not be treated as evidence that a risk occurred, legal advice, investment advice or an environmental performance score.

Personal And Sensitive Information

The dataset consists of public company filings. It is not designed to identify private individuals, although source filings may contain names or legal matter details when companies include them in public disclosures.

Licensing Information

The source texts come from public SEC filings. Users are responsible for complying with SEC EDGAR access terms and any rights that may apply to the source filing text.

Citation Information

Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H. and Steele, L. B. (2014). The information content of mandatory risk factor disclosures in corporate filings. Review of Accounting Studies, 19(1), 396-455. https://doi.org/10.1007/s11142-013-9258-3

Loughran, T. and McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries and 10-Ks. Journal of Finance, 66(1), 35-65. https://doi.org/10.1111/j.1540-6261.2010.01625.x