Dataset
ipo-text
Text of IPO filings, including S-1 and related prospectuses, in JSON: hundreds of thousands of records in English (CC BY 4.0). Pre-IPO disclosure text is one of the few information sources available ahead of a listing.
Data preview
Dataset card
The ipo-text dataset is a large-scale collection of parsed SEC S-1 and F-1 IPO filings compiled by the gtfintechlab research group, covering more than 100,000 documents across roughly two decades and over 20,000 registrants. It is distributed for machine-learning research tasks such as text classification, sentence similarity, and document question answering, and is released under a CC-BY-4.0 license.
What this dataset contains
The dataset holds parsed sections extracted from SEC EDGAR S-1 and F-1 IPO filings, with each filing represented by its company identifiers and three text sections: Prospectus Summary, Risk Factors, and Legal Matters. Only sections labeled 'Safe to Use' by the publisher's LLM-based classifier are retained, providing a quality-filtered corpus of long-form IPO disclosure text. The collection spans filings from 1994 through 2026 and encompasses more than 20,000 unique registrants.
Structure and fields
Records are delivered as a single tabular dataset with 109,690 rows in the published split, combining string identifiers and long text fields. Key columns include company, company_tickers, cik, sic, office, industry, exchanges, filing_type, filing_date, filing_accession_number, filing_url, and filing_images, together with validity flags and word counts for each parsed section such as parsed_prospectus_summary, parsed_risk_factors, and parsed_legal_matters. Helper fields like parsed_toc_keys and the boolean parsed_*_valid flags indicate which sections are present and usable for downstream modeling.
How analysts use it
Quantitative investment teams can use the parsed Risk Factors and Prospectus Summary fields as inputs for NLP models that score IPO disclosure tone, complexity, or topic coverage, supporting event-driven strategies around new listings. The company, cik, sic, and industry columns enable linking the text back to issuer fundamentals or to other SEC EDGAR datasets for cohort and time-series analysis. Because the corpus is structured by filing rather than by market outcome, it is best suited as a research and feature-engineering resource rather than a turnkey signal.
Documentation & references 1
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
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