Hugging Face's logo

Datasets:
ttchopper
/
openfundex

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
finance
value-investing
sec-filings
fundamental-analysis
tabular
DOI:
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
openfundex / README.md
Luke Danielson
Upload folder using huggingface_hub
b47ed08 verified
preview code
|
raw
history blame contribute delete
5.43 kB
metadata 
license: cc-by-4.0
language:
  - en
pretty_name: OpenFundex
tags:
  - finance
  - value-investing
  - sec-filings
  - fundamental-analysis
  - tabular
task_categories:
  - tabular-classification
  - tabular-regression
size_categories:
  - 100K<n<1M

OpenFundex Dataset

A structured dataset of SEC financial filings for deep value analysis and financial distress prediction.

Dataset Description

  • License: CC-BY-4.0
  • Language: English

Summary

OpenFundex contains financial statement data extracted from SEC EDGAR filings, enriched with derived financial metrics and labeled with established quality scores (Piotroski F-Score, Altman Z'-Score, Graham metrics). Designed for training ML models to assess company financial health and identify deep value opportunities.

Key design decision: This dataset uses only fundamental data from SEC filings. No market prices or equity trading data are included, eliminating survivorship bias.

Supported Tasks

  • Tabular Classification: Predict financial distress, value creation, fundamental improvement
  • Tabular Regression: Predict quality scores (f_score, z_prime_score, composite_quality_score), growth rates
  • Anomaly Detection: Identify companies in financial distress or with QA anomalies

Dataset Structure

Splits

Split Records Companies Date Range
train 221,779 11,786 2008-12-31 to 2019-12-31
validation 45,109 7,207 2020-01-31 to 2021-12-31
test 47,334 7,208 2022-01-31 to 2023-12-31
recent 38,171 6,358 2024-01-31 to 2025-11-30

Total records: 352,393

Feature Groups

Group Count
Identifiers 5
Context 4
Raw Features (SEC XBRL) 33
Derived Features 8
Engineered Features 23
QA Flags 4
Prediction Targets 19
Rank Targets 14

Scoring Models

  • Piotroski F-Score (0-9): Nine binary signals measuring profitability, leverage, and operating efficiency. Null when no prior quarter available for delta signals.
  • Altman Z'-Score (Float): Private-firm bankruptcy risk variant with zone classification (safe/grey/distress). Null for financial firms (SIC 6000-6999).
  • Beneish Coverage (0-8): Count of computable M-Score components. Full M-Score is computed transiently during enrichment but not retained.
  • Graham Metrics: Graham Number, NCAV/share, tangible book value/share, net working capital/share, defensive score (0-5).
  • Quality Signals: Cash conversion ratio, accrual ratio, free cash flow margin.
  • Composite Quality Score: Z-score normalized average of key quality signals within each quarter cross-section.

Target Columns

19 forward-looking prediction targets using same-quarter year-over-year comparisons:

1-Year Targets

  • Growth rates (6): BVPS, equity, earnings, revenue, OCF, FCF growth
  • Level/delta (2): Forward ROE, margin expansion
  • Binary (4): ROA improved, fundamentals improved (≥3 of 5 metrics), value created (equity grew AND ROE>0), survived

2-Year Targets

  • Growth rates (6): Same metrics as 1-year, over 2-year horizon
  • Binary (1): Survived 2 years

All targets are null when forward quarter data is unavailable.

Rank-Transformed Targets (14 columns)

Cross-sectional percentile ranks (0-1] for all Float64 targets, computed per quarter using rank("average") / count(). Raw growth targets are extremely skewed (mean ~3.1, median ~0.03) and produce negative information coefficients for regression models. Rank-transforming yields IC ~0.37.

Dataset Creation

Source Data

All data sourced exclusively from SEC EDGAR Financial Statement Data Sets (FSDS), 2009-present. No market data providers. No third-party data. No equity pricing data.

Pipeline

  1. Ingest: Download quarterly SEC FSDS ZIP files from EDGAR
  2. Parse: Extract XBRL financial data, normalize 32 tags to standard fields
  3. Enrich: Compute derived ratios (8), scoring models (5), and QA flags
  4. Label: Generate 19 forward-looking prediction targets
  5. Split: Temporal train/validation/test/recent splits with leakage validation
  6. Evaluate: Quality checks, ML fitness, and publication readiness
  7. Publish: Stage and upload to Hugging Face Hub

Considerations

Known Limitations

  • XBRL coverage varies: some companies report fewer standardized tags
  • F-Score delta components require prior quarter data (null for first appearance)
  • Z'-Score was designed for manufacturing firms; interpretation varies by sector
  • No market data: cannot compute price-based metrics (P/E, market cap, etc.)

Bias Considerations

  • No survivorship bias: Uses only SEC filing data, not equity market prices
  • Temporal integrity: Strict time-based splits prevent data leakage
  • Sector bias: Z'-Score thresholds may not be equally applicable across all sectors
  • Financial firms excluded from Z'-Score: Financial companies (SIC 6000-6999) have null Z'-Score values

License

This dataset is released under the CC-BY-4.0 license.

The underlying SEC data is in the public domain.

Citation 

@dataset{openfundex,
  title={OpenFundex: SEC Financial Filings for Deep Value Analysis},
  author={Danielson, Luke},
  year={2026},
  url={https://github.com/danielukea/openfundex},
  license={CC-BY-4.0}
}