The Fama-French Data Library publishes documented factor, portfolio, breakpoint, and research-return datasets for asset-pricing analysis.
The Fama-French Data Library is Kenneth French’s public collection of factor returns, research portfolios, breakpoints, and related asset-pricing datasets. Researchers and analysts use it to estimate factor exposures, reproduce academic tests, and compare portfolio returns with documented benchmarks. The files are research data, not investment recommendations or immutable market records.
The exact catalog changes over time. Major groups include:
| Dataset group | Examples | Typical analytical use |
|---|---|---|
| Research factors | Market excess return, SMB, HML, profitability, investment, momentum | Factor regression and return attribution |
| Sorted portfolios | Portfolios formed on size, book-to-market, profitability, investment, or prior returns | Testing cross-sectional patterns and model fit |
| Multi-dimensional sorts | 2 x 3, 5 x 5, and other combinations | Examining interactions between characteristics |
| Breakpoints | Market equity, book-to-market, profitability, investment, and prior-return cutoffs | Reproducing portfolio assignment rules |
| Industry portfolios | Portfolios grouped by industry classifications | Industry controls, benchmarking, and research tests |
| International data | Developed, regional, and selected emerging-market factors and portfolios | Analysis outside the U.S. dataset |
| Historical archives | Prior data cuts for selected U.S. factors and construction portfolios | Measuring the effect of later data revisions |
Availability, history, and frequency differ by dataset. The main catalog and each details page are the controlling references.
A standard monthly three-factor dataset commonly includes:
The source documentation should be checked for units and conventions before calculation. Factor files commonly present returns as percentages, so a displayed value of 2.10 is normally converted to 0.0210 for software expecting decimal returns.
Consider this invented monthly row, which is not an observation from the library:
| Date | Mkt-RF | SMB | HML | RF |
|---|---|---|---|---|
| 202501 | 2.10 | -0.40 | 1.20 | 0.35 |
If the values are percentages, the broad market return represented by the row is:
For a regression package that expects decimal returns, the input values would be 0.0210, -0.0040, 0.0120, and 0.0035. Mixing percentage and decimal units creates coefficients and intercepts off by a factor of 100.
The Fama-French Three-Factor Model explains how these series enter a return regression.
The official details page describes the U.S. stock factors as follows:
These are construction rules, not plain-language labels. SMB is not simply the return of a small-cap index, and HML is not simply the return of a value-stock fund.
Do not substitute a U.S. factor file for a developed-market or regional analysis without a stated reason. International factor documentation should be checked for the return currency, regional breakpoints, and market universe.
Match daily, weekly, or monthly factors to the asset-return frequency. Converting one frequency to another requires correct compounding and date alignment; repeating a monthly return across daily rows is not valid.
The three-factor, five-factor, and momentum datasets answer different model questions. Adding every available series can create multicollinearity and data-mining problems. Choose factors before inspecting the desired result and justify each inclusion.
Factor returns, sorted portfolio returns, and breakpoint files are different products. A factor is generally a return difference between constructed portfolios. A sorted portfolio is a funded return series. A breakpoint is a classification threshold, not a return.
Some portfolio datasets have versions excluding dividends. Confirm which version matches the research question and do not compare ex-dividend returns with total-return benchmarks without adjustment.
This process is more defensible than downloading a current file later and assuming it is identical to the one used in the original analysis.
The library states that U.S. research returns can change when CRSP revises its source database and that the full return history is reconstructed during updates. It provides historical archives for selected monthly factor and construction-portfolio files so users can compare prior data cuts.
The library also documents a significant source-format transition. CRSP’s legacy Stock and Indexes format was discontinued after the December 2024 release, and U.S. research returns beginning with the January 2025 release use CRSP’s Flat File Format 2.0. The library notes that monthly return construction differs between the old and new formats, including the timing of dividend reinvestment.
This does not make either history unusable. It means a reproduced result should identify the data vintage, and an unexplained difference may arise from a source revision rather than an error in the regression code.
The library states that missing data can be represented by values such as -99.99 or -999. These are sentinels, not extreme returns. Convert them to missing values before calculating means, compounding returns, or estimating a model.
Other controls include:
Suppose an analyst wants to estimate whether a U.S. equity fund had market, size, and value exposure over a five-year monthly window. A defensible setup would:
The regression output remains descriptive. A positive historical HML loading does not guarantee future value exposure or return.
The library is an important research resource, but its series inherit limitations from source data, accounting definitions, portfolio formation rules, and historical revisions. Results can be sensitive to sample dates, frequency, test assets, factor selection, and statistical method.
Public availability does not eliminate licensing or redistribution questions for downstream products. Users should review the library’s notices and the terms governing any underlying data before redistributing files or building a commercial data service.
This article provides general financial education and research-data guidance. It does not recommend a dataset, model, fund, factor allocation, security, or trading strategy.