ipo-mine 0.0.0

Mining and parsing S-1 IPO filings

IPO-Mine: S-1 (IPO) Filings Toolkit

GitHub Repository: https://github.com/gtfintechlab/S1-Filings
Project Website: https://ipo-mine.web.app/

Overview

IPO-Mine is a Python package for downloading, parsing, and structuring S-1 IPO filings from the U.S. Securities and Exchange Commission (SEC) EDGAR system.

This repository implements the data processing pipeline used to construct the IPO-Mine dataset, a section-structured corpus introduced in the research paper:

IPO-Mine: A Section-Structured Dataset for Analyzing Long and Complex IPO Filings

The objective of this project is to transform raw SEC filings into clean, standardized, and section-aligned textual representations suitable for large-scale analysis in natural language processing, information retrieval, and long-document modeling.

Motivation

S-1 filings are among the most complex regulatory documents used in empirical research. They exhibit several challenges:

• Extreme document length, often exceeding 100–300 pages
• Substantial variation in section headers across firms and time
• Heterogeneous formats, including HTML, plain text, and scanned images
• Limited structural consistency despite regulatory guidance

These characteristics complicate tasks such as section segmentation, cross-firm comparison, longitudinal analysis, and long-context modeling.

IPO-Mine addresses these challenges by providing a unified and reproducible pipeline that converts raw EDGAR filings into structured, research-ready data.

Features

• Automated downloading of S-1 and S-1/A filings from SEC EDGAR
• Parsing of Tables of Contents (TOCs) for filings dating back to 1997
• Extraction and normalization of key IPO sections, including:
  • Risk Factors
  • Business
  • Use of Proceeds
  • Management’s Discussion and Analysis (MD&A)
  • Financial Statements
• Support for multiple filing formats:
  • HTML
  • plain text
  • image-based filings via OCR
• Fuzzy matching of section headers using global section mappings
• Deterministic outputs suitable for reproducible dataset construction

IPO-Mine Dataset

Using this toolkit, the IPO-Mine dataset is constructed as a large-scale corpus of IPO filings with:

• Section-aligned text across firms
• Standardized section nomenclature
• Clean document boundaries
• Compatibility with long-document modeling and retrieval frameworks

Additional details and examples are available at:

https://ipo-mine.web.app/

Installation

The package is available on PyPI under the name ipo-mine.

pip install ipo-mine

OCR Dependency

Parsing image-based filings requires a local installation of Tesseract OCR.

Tesseract Installation

Operating System	Installation Method
macOS	brew install tesseract
Ubuntu / Debian	sudo apt install tesseract-ocr
Windows	UB Mannheim Tesseract installer
Conda environments	Included automatically

Example Usage

from ipo_mine.download.company import Company
from ipo_mine.download import S1Downloader
from ipo_mine.parse.s1_parser import S1Parser
from ipo_mine.resources import GLOBAL_SECTIONS_JSON
from ipo_mine.utils.config import PARSED_DIR

downloader = S1Downloader(
    email="[e-mail]",
    company="Your Institution"
)

ticker = "SNOW"
filing = downloader.download_s1(Company.from_ticker(ticker))

parser = S1Parser(
    filing=filing,
    mappings_path=GLOBAL_SECTIONS_JSON,
    output_base_path=PARSED_DIR
)

risk_factors = parser.parse_section("Risk Factors", ticker)

Research-Oriented Design

This library is designed primarily for dataset construction and reproducible empirical research rather than ad-hoc scraping.

Typical use cases include:

• Building section-aligned IPO corpora
• Comparing disclosure language across firms and time
• Training and evaluation of long-document language models
• Large-scale studies of regulatory disclosures

Citation

If you use this package or the IPO-Mine dataset in your research, please cite:

@inproceedings{ipomine2025,
  title     = {IPO-Mine: A Section-Structured Dataset for Analyzing Long and Complex IPO Filings},
  author    = {Author names},
  booktitle = {Proceedings of the ACM SIGKDD Conference},
  year      = {2025}
}

License

This project is released under the MIT License.