VOL. XCIV, NO. 247
★ A CURATED DIRECTORY OF FINANCIAL TOOLS AND RESOURCES ★
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Global Factor Data (JKP Factors) Review, Pricing, and Features
Research dataset + web interface for global equity factor returns and stock-level characteristics based on Jensen, Kelly, and Pedersen (Journal of Finance, 2023). Website is updated regularly (latest update includes data through Dec 2024). Code/data are free for academic research and personal non-commercial use; other uses (incl. redistribution) require consent.
Community votes (overall)
Pricing
Free
Free (non-commercial license)
Free for academic research and personal non-commercial use; other use (incl. redistribution) requires prior consent.
Key features
- Web UI provides access to factor returns and related resources based on "Is There a Replication Crisis in Finance?" (Journal of Finance, 2023).
- Factor-returns library is presented as 153 factors (characteristics) grouped into 13 themes, covering data from 93 countries (and "four regions" on the factor-returns page).
- Factor returns are available at daily and monthly frequencies, with equal-weighted, value-weighted, or capped-value-weighted options; returns are excess returns in USD.
- Documentation states "Global Factor Data includes 406 characteristics and their associated factor portfolios" (a superset of the 153 factors analyzed in the 2023 paper).
- Methodology detail (documentation): within each country-month, stocks are sorted into characteristic terciles using non-micro breakpoints; "capped value weight" uses market equity winsorized at the NYSE 80th percentile; factor is high-tercile minus low-tercile; non-missing requires at least 5 stocks in each leg and at least 60 valid monthly observations for inclusion.
- Stock characteristics page: monthly stock-level characteristics data can be downloaded from WRDS; the page supports selecting multiple characteristics and viewing raw vs market-adjusted returns.
- Analysis page includes "Factors to Watch" plus advanced filters and a performance table with metrics like Sharpe ratio, average returns, volatility, information ratio, alpha, and beta; also supports comparing cumulative performance across regions/themes/weighting schemes.
- Release notes describe a SAS/R -> Python migration: modular code leveraging Polars and DuckDB, single-command execution, reproducible environments via uv, additional outputs (e.g., standardized quarterly/annual accounting data, daily/monthly FF3 & HXZ4 factors, GICS industry returns), and modern outputs moving from CSV to Parquet.
- GitHub repo provides Python code to generate factor returns, stock returns, and firm characteristics; default end date in code is 2024-12-31; authors note running on HPC with ~450GB RAM / 128 CPU cores and ~6 hours runtime.
- CTF section: provides rules and WRDS dataset-access instructions for extracting CTF tables from WRDS (including prerequisites for CRSP monthly/daily stock files and Compustat North America).
Data partners
Primary access/distribution channel referenced for stock characteristics (and for CTF tables) via WRDS database tables.
CTF/WRDS access prerequisites explicitly list CRSP Monthly Stock File and CRSP Daily Stock File access requirements.
CTF/WRDS access prerequisites explicitly list Compustat North America access requirements; documentation references Compustat datasets for accounting variables.
FAQ
Is Global Factor Data (JKP Factors) free?
Global Factor Data (JKP Factors) is free to use. Visit the vendor site for the latest limits and onboarding details.
Who is Global Factor Data (JKP Factors) best for?
Global Factor Data (JKP Factors) is built for Quants/Developers, Analysts, and Students/Researchers. It suits advanced users.
What platforms and connections does Global Factor Data (JKP Factors) support?
You can use Global Factor Data (JKP Factors) on Web and Other. Integrations include WRDS and GitHub.
Which markets does Global Factor Data (JKP Factors) cover?
Global Factor Data (JKP Factors) covers North America, Europe, APAC, LatAm, Middle East, and Africa. It tracks Stocks.
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About Me
Hey! Thanks for stopping by. I'm an independent investor with a soft spot for coding and the stock market - especially value investing (as you can probably tell from the tools I've collected here, haha). Hopefully you'll find something useful or interesting along the way.
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