---
pretty_name: Tidy Finance Factor Library Specification Grid
license: cc0-1.0
language:
- en
tags:
- finance
- asset-pricing
- factor-models
- portfolio-sorts
- empirical-finance
size_categories:
- 100K<n<1M
---

# Tidy Finance Factor Library: Specification Grid

Lookup table mapping specification IDs to portfolio sorting configurations. Use this dataset together with the [Portfolio Returns](https://huggingface.co/datasets/tidy-finance/portfolio-returns) dataset to identify the methodological choices behind each factor return series.

## Dataset Details

### Dataset Description

The dataset contains approximately 960,000 unique specification paths for constructing long-short portfolio returns. Each row defines a complete set of preprocessing and sorting choices (sample exclusions, lagging convention, breakpoint definition, weighting scheme, rebalancing frequency). The `id` column links to the corresponding return series in the Portfolio Returns dataset.

- **Curated by:** Christoph Frey (Lancaster University), Christoph Scheuch (Tidy Intelligence), Stefan Voigt (University of Copenhagen), Patrick Weiss (Reykjavík University)
- **Funded by:** Danish Finance Institute
- **License:** CC0 1.0

### Dataset Sources

- **Repository:** [https://github.com/tidy-finance/jss-multilingual-factor-library](https://github.com/tidy-finance/jss-multilingual-factor-library)
- **R package:** [https://github.com/tidy-finance/r-tidyfinance](https://github.com/tidy-finance/r-tidyfinance)
- **Python package:** [https://github.com/tidy-finance/py-tidyfinance](https://github.com/tidy-finance/py-tidyfinance)
- **Demo:** [https://app-download-center.cloud.sdu.dk/](https://app-download-center.cloud.sdu.dk/)

## Uses

### Direct Use

- Joining with the Portfolio Returns dataset to filter or group factor returns by specific methodological choices.
- Robustness and sensitivity analysis: selecting subsets of specifications to study how preprocessing decisions affect factor premia.
- Replication: documenting the exact configuration behind a reported result.

### Out-of-Scope Use

- Standalone analysis. The grid contains no return data and must be joined with the Portfolio Returns dataset via the `id` column.

## Dataset Structure

The dataset consists of a single Parquet file with 14 columns and approximately 960,000 rows.

| Column | Type | Description |
|--------|------|-------------|
| `id` | int32 | Unique specification identifier, foreign key to the Portfolio Returns dataset |
| `sorting_variable` | string | Sorting characteristic (e.g., `sv_ag` for asset growth, `sv_bm` for book-to-market) |
| `min_size_quantile` | double | Minimum NYSE size quantile for sample inclusion: `NA` (none) or `0.2` (bottom 20th percentile excluded) |
| `exclude_financials` | bool | Whether financial firms (SIC 6000-6799) are excluded |
| `exclude_utilities` | bool | Whether utility firms (SIC 4900-4999) are excluded |
| `exclude_negative_earnings` | bool | Whether firms with negative earnings are excluded |
| `sorting_variable_lag` | string | Lagging convention: `3m`, `6m`, or `ff` (Fama-French) |
| `rebalancing` | string | Rebalancing frequency: `monthly` or `annual` (July) |
| `n_portfolios_main` | double | Number of quantile portfolios for the primary sort: `3`, `5`, or `10` |
| `sorting_method` | string | Sorting method: `univariate`, `bivariate-dependent`, or `bivariate-independent` |
| `breakpoints_min_size_threshold` | double | Minimum NYSE size quantile used when computing breakpoints: `NA` (none) or `0.2` |
| `n_portfolios_secondary` | double | Number of quantile portfolios for the secondary sort (size): `2`, `5`, or `NA` for univariate sorts |
| `breakpoints_exchanges` | string | Exchanges used for breakpoint computation: `NYSE` or `AMEX\|NASDAQ\|NYSE` |
| `weighting_scheme` | string | Portfolio weighting: `EW` (equal-weighted), `VW` (value-weighted), or `capped VW` |

## Dataset Creation

### Curation Rationale

Factor construction involves many subjective methodological choices. Rather than committing to a single specification, we enumerate all valid combinations to enable systematic robustness analysis and transparent reporting.

### Source Data

#### Data Collection and Processing

The grid is generated programmatically from the full factorial combination of preprocessing choices, with invalid configurations removed (e.g., univariate sorts have no secondary breakpoints; market equity is excluded from bivariate sorts where size is the secondary variable; earnings-to-market excludes configurations that allow negative earnings). See `code/01_define_portfolio_sorts_grid.R` in the companion repository for the exact generation logic.

#### Who are the source data producers?

The grid is a methodological artifact created by the dataset authors. No external data sources are involved.

### Personal and Sensitive Information

The dataset contains no personal or sensitive information. All columns describe portfolio sorting configurations.

## Bias, Risks, and Limitations

- The grid reflects the authors' choice of specification dimensions and does not cover all possible methodological variations (e.g., alternative industry classifications, different minimum listing requirements, or alternative risk-free rate definitions).
- Some specifications may produce portfolios with very few stocks in certain months, particularly for smaller sorting variables or restrictive exclusion criteria.

### Recommendations

Always join with the Portfolio Returns dataset via the `id` column. When reporting results, cite the specific `id` or the full set of column values to ensure reproducibility.

## Citation

**BibTeX:**

```bibtex
@article{frey2026transparent,
  title={A Transparent Financial Risk Factor Library},
  author={Frey, Christoph and Scheuch, Christoph and Voigt, Stefan and Weiss, Patrick},
  year={2026},
  journal={Working Paper}
}
Dataset Card Authors
Christoph Frey, Christoph Scheuch, Stefan Voigt, Patrick Weiss

Dataset Card Contact
Stefan Voigt (stefan.voigt@econ.ku.dk), Patrick Weiss (patrickw@ru.is)



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