tidy-finance/factor-library

Tidy Finance Factor Library: Portfolio Returns

Long-short portfolio returns for 50 financial risk factors across a comprehensive grid of methodological specifications. The dataset covers US equities from 1960 to 2024.

Dataset Details

Dataset Description

The dataset contains monthly long-short portfolio returns for 50 sorting variables commonly used in empirical asset pricing. Each sorting variable is evaluated across all valid combinations of preprocessing choices (sample exclusions, lagging conventions, breakpoint definitions, weighting schemes, and rebalancing frequencies), yielding hundreds of specification paths per variable. The selection of sorting variables follows the cross-section of characteristics studied by Jensen, Kelly, and Pedersen (2023).

• Curated by: Christoph Frey (Lancaster University), Christoph Scheuch (Tidy Intelligence), Stefan Voigt (University of Copenhagen), Patrick Weiss (Reykjavík University)
• License: MIT

Dataset Sources

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

Uses

Direct Use

• Empirical asset pricing research: evaluating factor models, testing anomalies, and benchmarking portfolio strategies.
• Robustness analysis: comparing factor returns across different methodological specifications.
• Teaching and replication: reproducing canonical results from the asset pricing literature.
• Sensitivity analysis: studying how preprocessing choices (breakpoints, weighting, rebalancing) affect factor premia.

Out-of-Scope Use

• Live trading signals. The dataset reflects historical, backward-looking portfolio returns and does not account for transaction costs, market impact, or real-time data availability.
• Causal inference about individual firm outcomes.

Dataset Structure

The dataset consists of 150 Parquet files with 6 columns. The id column maps to a separate specification grid that documents all preprocessing choices for each specification (breakpoints, exclusions, rebalancing frequency, etc.).

Column	Type	Description
id	int32	Unique specification identifier linking to the portfolio sort grid
date	date32	Month of the portfolio return
ret_type	string	Return type: vw (value-weighted) or ew (equal-weighted)
ret	double	Monthly long-short portfolio excess return
sorting_variable	string	Sorting characteristic (e.g., ag for asset growth, bm for book-to-market)
sorting_variable_lag	string	Lagging convention: 3m, 6m, or ff (Fama-French)

The sorting_variable partition key identifies the characteristic (e.g., ag for asset growth, bm for book-to-market). The sorting_variable_lag partition key identifies the lagging convention (3m, 6m, or ff for the Fama-French convention). The id column maps to a separate specification grid that documents all preprocessing choices for each specification (breakpoints, exclusions, rebalancing frequency, etc.).

Sorting variables

The 50 sorting variables span seven categories: investment, profitability, valuation, momentum, intangibles, trading frictions, and financing. See the companion paper for the full list and definitions.

Specification grid

Each sorting variable is evaluated across combinations of:

Choice	Options
Size exclusion	None; bottom 20th NYSE percentile
Exclusion of financials	No; Yes
Exclusion of utilities	No; Yes
Exclusion of neg. earnings	No; Yes
Sorting variable lag	3 months; 6 months; Fama-French
Rebalancing	Monthly; Annual (July)
Breakpoint quantiles (main)	5; 10
Double sort	Single; Dependent; Independent
Breakpoint quantiles (secondary)	2; 5
Breakpoint exchanges	NYSE; All
Weighting	Equal-weighted; Value-weighted

Dataset Creation

Curation Rationale

Existing factor libraries (e.g., the French Data Library) provide canonical factor series but without code or detailed workflows, limiting reproducibility and methodological comparison. We constructed this dataset to offer a transparent, fully replicable factor library that covers a comprehensive grid of specification choices.

Source Data

Data Collection and Processing

Raw data comes from CRSP (monthly stock returns, market capitalization, exchange, industry) and Compustat (annual financial statements) via WRDS. The tidyfinance R package handles all data acquisition, cleaning, variable construction, and portfolio aggregation. Preprocessing steps include restricting to common equity of US-incorporated corporate issuers on major exchanges, computing excess returns relative to the Fama-French risk-free rate, and constructing sorting variables with appropriate lagging conventions. See the companion paper and the tidyfinance package documentation for full details.

Who are the source data producers?

• CRSP (Center for Research in Security Prices) at the University of Chicago
• Compustat (S&P Global Market Intelligence)

Personal and Sensitive Information

The dataset contains aggregated portfolio-level returns only. No individual-level, personal, or sensitive information is included.

Bias, Risks, and Limitations

• Survivorship and look-ahead bias: We follow standard academic conventions (e.g., Fama-French lagging) to mitigate look-ahead bias, but the dataset reflects the CRSP/Compustat universe with its known survivorship characteristics.
• US equities only: The current version covers US-listed stocks. International markets are not included.
• Historical data: Returns reflect past market conditions and do not predict future performance.
• Data vendor dependence: Reproducing the dataset from scratch requires WRDS access (CRSP and Compustat subscriptions).

Recommendations

Researchers should consult the companion paper for detailed variable definitions and the specification grid documentation before using the data. When reporting results, specify the exact specification path (identified by id) to ensure reproducibility.

Citation

BibTeX:

@article{Frey.2026,
  title={A Transparent Financial Risk Factor Library},
  author={Frey, Christoph and Scheuch, Christoph and Voigt, Stefan and Weiss, Patrick},
  year={2026},
  journal={Working Paper}
}