TL;DR
The Ken French Data Library provides monthly returns for the size, value, profitability, investment and momentum factors back to 1926 (US) and 1990 (international). It's the academic source of truth for factor investing.
In short
Every credible factor study uses Ken French data because the methodology is transparent, replicable, and decades old. The factors are constructed as long-short portfolios sorted on specific characteristics. Real-world factor ETFs typically capture 60-80% of the raw factor premiums after costs.
We're working on a full deep-dive for this article — including historical data, charts, and worked examples. In the meantime, you can run a free simulation to explore the underlying numbers yourself.
Frequently asked questions
- How can I access the data?
- Free at mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. CSV format, monthly granularity, no registration.
- Why do real funds underperform Ken French factors?
- Trading costs, fund expenses, real-world constraints on shorting, and the fact that the academic portfolios are notional rebalanced monthly with no friction.
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