---
license: mit
task_categories:
  - tabular-classification
tags:
  - finance
  - xbrl
  - distress-prediction
  - sec-edgar
  - tabular
  - financial-statements
pretty_name: "Fin-JEPA Study 0: XBRL Financial Distress Dataset"
size_categories:
  - 10K<n<100K
language:
  - en
configs:
  - config_name: default
    data_files:
      - split: train
        path: default/train.parquet
      - split: validation
        path: default/validation.parquet
      - split: test
        path: default/test.parquet
    default: true
dataset_info:
  - config_name: default
    features:
      - name: cik
        dtype: string
      - name: ticker
        dtype: string
      - name: fiscal_year
        dtype: int64
      - name: period_end
        dtype: date32
      - name: filed_date
        dtype: date32
      - name: sector
        dtype: string
      - name: sic_code
        dtype: string
      - name: total_assets
        dtype: float64
      - name: total_liabilities
        dtype: float64
      - name: total_equity
        dtype: float64
      - name: current_assets
        dtype: float64
      - name: current_liabilities
        dtype: float64
      - name: retained_earnings
        dtype: float64
      - name: cash_equivalents
        dtype: float64
      - name: total_debt
        dtype: float64
      - name: total_revenue
        dtype: float64
      - name: cost_of_sales
        dtype: float64
      - name: operating_income
        dtype: float64
      - name: net_income
        dtype: float64
      - name: interest_expense
        dtype: float64
      - name: cash_from_operations
        dtype: float64
      - name: cash_from_investing
        dtype: float64
      - name: cash_from_financing
        dtype: float64
      - name: stock_decline
        dtype: int8
      - name: earnings_restate
        dtype: int8
      - name: audit_qualification
        dtype: int8
      - name: sec_enforcement
        dtype: int8
      - name: bankruptcy
        dtype: int8
      - name: earnings_restate_source
        dtype: string
      - name: fwd_ret_252d
        dtype: float64
      - name: mkt_adj_252d
        dtype: float64
      - name: delisted
        dtype: bool
      - name: period_end_xbrl
        dtype: date32
      - name: period_end_label
        dtype: date32
    splits:
      - name: train
        num_examples: 14748
      - name: validation
        num_examples: 6066
      - name: test
        num_examples: 14895
---

# Fin-JEPA Study 0: XBRL Financial Distress Dataset

A tabular dataset of **35,709** company-year observations for
**4,520** unique SEC filers (2012--2023), linking XBRL financial
statement features extracted from 10-K filings to five binary distress
outcomes. Built for the first gate of the
[Financial JEPA](https://github.com/elroy-galbraith/fin-jepa) project.

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("elroyg/fin-jepa-study0")
print(ds)
# DatasetDict({
#     train: Dataset(num_rows=14748, ...),
#     validation: Dataset(num_rows=6066, ...),
#     test: Dataset(num_rows=14895, ...),
# })

train = ds["train"].to_pandas()
print(train.columns.tolist())
```

## Dataset Structure

### Configs

| Config | Description | Files |
|--------|-------------|-------|
| `default` | Merged features + labels, temporally split | `default/{train,validation,test}.parquet` |
| (raw files) | Individual source parquets for advanced use | `raw/*.parquet` (see below) |

### Columns (34 total, including 2 audit columns)

**Identifiers / Metadata (7)**

| Column | Type | Description |
|--------|------|-------------|
| `cik` | string | SEC Central Index Key (10-digit, zero-padded) |
| `ticker` | string | Equity ticker symbol (nullable) |
| `fiscal_year` | int64 | Fiscal year of the 10-K filing |
| `period_end` | date | Fiscal year-end date |
| `filed_date` | date | Date the 10-K was filed with the SEC |
| `sector` | string | Fama-French 12-industry sector |
| `sic_code` | string | 4-digit SIC code (nullable) |

**XBRL Financial Features (16)** --- all `float64`, sourced from SEC EDGAR Company Facts API

| Column | Statement | XBRL Concept(s) |
|--------|-----------|-----------------|
| `total_assets` | Balance Sheet | Assets |
| `total_liabilities` | Balance Sheet | Liabilities |
| `total_equity` | Balance Sheet | StockholdersEquity (+fallback) |
| `current_assets` | Balance Sheet | AssetsCurrent |
| `current_liabilities` | Balance Sheet | LiabilitiesCurrent |
| `retained_earnings` | Balance Sheet | RetainedEarningsAccumulatedDeficit |
| `cash_equivalents` | Balance Sheet | CashAndCashEquivalentsAtCarryingValue (+fallbacks) |
| `total_debt` | Balance Sheet | Computed: LongTermDebt + ShortTermBorrowings |
| `total_revenue` | Income | Revenues (+fallbacks) |
| `cost_of_sales` | Income | CostOfGoodsSold (+fallbacks) |
| `operating_income` | Income | OperatingIncomeLoss |
| `net_income` | Income | NetIncomeLoss (+fallbacks) |
| `interest_expense` | Income | InterestExpense (+fallback) |
| `cash_from_operations` | Cash Flow | NetCashProvidedByUsedInOperatingActivities (+fallback) |
| `cash_from_investing` | Cash Flow | NetCashProvidedByUsedInInvestingActivities (+fallback) |
| `cash_from_financing` | Cash Flow | NetCashProvidedByUsedInFinancingActivities (+fallback) |

> Features are **un-normalised** (raw USD values as reported). NaN means the
> concept was not reported in the filing. See the
> [feature engineering module](https://github.com/elroy-galbraith/fin-jepa/blob/main/src/fin_jepa/data/feature_engineering.py)
> for ratio computation, YoY changes, winsorisation, and quantile normalisation.

**Distress Labels (5)** --- nullable `Int8` (0 = no event, 1 = event, NaN = unavailable)

| Column | Definition | Source | Positive Rate |
|--------|-----------|--------|--------------|
| `stock_decline` | Market-adjusted 252-day return < -20% | yfinance (forward returns from filing date) | 43.2% |
| `earnings_restate` | Either (a) a 10-K/A-family filing (10-K/A, 10-KT/A, NT 10-K/A) within 1095 days of `period_end` in the EDGAR index, or (b) the XBRL Company Facts API reports ≥2 10-K filings for the same `(cik, period_end)`. See `earnings_restate_source` for per-row provenance. | Reconciled: EDGAR quarterly index + XBRL amendment registry | 22.3% |
| `audit_qualification` | Going-concern or adverse audit opinion | External CSV (Audit Analytics) | 0.0% |
| `sec_enforcement` | SEC enforcement action disclosure | EDGAR EFTS full-text search (noisy proxy) | 0.5% |
| `bankruptcy` | Chapter 7/11 filing (8-K Item 1.03) | EDGAR EFTS full-text search | 0.8% |

> **NaN semantics:** NaN means "data unavailable", **not** "no event".
> Training code must mask NaN labels in the loss function.
>
> **Delisted companies:** Firms with fewer than 252 trading days of data
> after their filing date are marked `delisted=True` and assigned
> `stock_decline=1`.
>
> **`earnings_restate` reconciliation (v1.1):** The v1.0 label was a narrow
> 10-K/A proxy within a 365-day window, and only ~9% of XBRL-detected
> amendments carried `earnings_restate=1`.  v1.1 reconciles the two signals:
> the label fires if either (a) a 10-K/A-family filing (including
> `NT 10-K/A` and `10-KT/A`) appears in the EDGAR index within 1095 days
> of `period_end`, or (b) the XBRL Company Facts API reports multiple
> 10-K filings for the same `(cik, period_end)`.  The `earnings_restate_source`
> column records per-row which signal fired (`edgar`, `xbrl`, `both`, or
> `none`) so consumers can reproduce v1.0 by filtering to
> `earnings_restate_source in ("edgar", "both")` with the narrow horizon
> applied offline, or ablate by source in Study 1.  This is still a proxy
> — for ground truth, use Audit Analytics.
>
> **`audit_qualification` coverage:** This label may be all-NaN if no
> external Audit Analytics data was available at build time.

**Market Context (3)**

| Column | Type | Description |
|--------|------|-------------|
| `fwd_ret_252d` | float64 | Log return over 252 trading days from filing date |
| `mkt_adj_252d` | float64 | Market-adjusted return (stock - S&P 500) |
| `delisted` | bool | True if < 252 trading days remain post-filing |

**Audit Columns (2)** --- for diagnosing date mismatches between pipelines

| Column | Type | Description |
|--------|------|-------------|
| `period_end_xbrl` | date | Fiscal year-end date from XBRL filing metadata (= `period_end`) |
| `period_end_label` | date | Fiscal year-end date from the label/market pipeline |

> The XBRL and label pipelines sometimes disagree on the canonical fiscal
> year-end date by a few days (e.g. Dec 29 vs Dec 26 for companies whose
> fiscal year ends on the last Saturday of December). The dataset joins on
> `(cik, fiscal_year)` and uses the XBRL date as canonical `period_end`.
> Rows where the two dates differ by more than 14 days are dropped as
> mismatches. These audit columns let you verify alignment.

## Temporal Splits

Splits are **strictly temporal** to prevent look-ahead bias:

| Split | Period End Range | Rows | Purpose |
|-------|-----------------|------|---------|
| `train` | &le; 2017-12-31 | 14,748 | Model training |
| `validation` | 2018-01-01 to 2019-12-31 | 6,066 | Hyperparameter tuning |
| `test` | 2020-01-01 to 2023-12-31 | 14,895 | Final evaluation |

> The test period covers COVID-19, high-inflation, and rising-rate regimes,
> testing out-of-distribution generalisation.

## Data Sources

All data is derived from **public, free sources**:

| Component | Source | URL |
|-----------|--------|-----|
| XBRL Features | SEC EDGAR Company Facts API | `https://data.sec.gov/api/xbrl/companyfacts/` |
| Filing Index | SEC EDGAR Quarterly Index | `https://www.sec.gov/Archives/edgar/full-index/` |
| Market Data | Yahoo Finance (via yfinance) | `https://finance.yahoo.com/` |
| SEC Enforcement | EDGAR EFTS Full-Text Search | `https://efts.sec.gov/LATEST/search-index` |
| Bankruptcy | EDGAR EFTS (8-K Item 1.03) | `https://efts.sec.gov/LATEST/search-index` |

The universe includes **delisted, bankrupt, and acquired companies** ---
there is no survivorship bias.

## Raw Files

For advanced users who want the individual source tables, four parquets
are available in the `raw/` directory:

```python
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="elroyg/fin-jepa-study0",
    repo_type="dataset",
    filename="raw/xbrl_features.parquet",
)
```

| File | Rows | Description |
|------|------|-------------|
| `raw/xbrl_features.parquet` | ~45k | 16 raw XBRL features per company-year |
| `raw/label_database.parquet` | ~45k | 5 binary distress labels |
| `raw/company_universe.parquet` | ~14k | Company metadata (one row per filer) |
| `raw/market_aligned.parquet` | ~45k | Forward returns at 4 horizons + market benchmarks |

## Citation

```bibtex
@dataset{galbraith2025finjepa,
  author    = {Galbraith, Elroy},
  title     = {Fin-JEPA Study 0: XBRL Financial Distress Dataset},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/elroyg/fin-jepa-study0}
}
```

## License

MIT