Next free-source hunt
We check for new free financial data every Wednesday.
Datasets / Academic Data
mba.tuck.dartmouth.edu/

Kenneth French Data Library

Freeauth: noneCustom#academic#factor-research✓ verified Oct 28, 2026

Kenneth French Data Library

Free downloadable CSV/TXT files of Fama-French factor returns and research portfolios. No API, no key required; direct file downloads from Dartmouth Tuck School of Business.

Kenneth French's Data Library at Dartmouth's Tuck School publishes the Fama/French factor returns and research portfolios as free TXT/CSV zip downloads, with no API, key or account.

Method change in 2025: CRSP discontinued its Legacy (FIZ) files after the December 2024 release. Since the January 2025 release, US returns are built from CRSP's new CIZ files, and monthly returns are compounded daily returns with dividends reinvested on their ex-dates (FIZ reinvested at month-end). The pre-2025 FIZ history is kept in a separate archive, so results can differ from older papers or cached downloads; re-download before comparing.

The library reconstructs the full return history each month when it updates the portfolios, so past values can change (for example, if CRSP revises its database). The one-month Treasury bill rate (RF) comes from the ICE BofA US 1-Month Treasury Bill Index from June 2024 (Ibbotson Associates before). Files are copyrighted by Eugene F. Fama and Kenneth R. French, and no reuse license is stated.

Coverage

US and international factor returns (Fama/French 3- and 5-factor, momentum, short- and long-term reversal) and research portfolios sorted on size, value, profitability, investment and more, plus industry portfolios. International: Developed, Developed ex US, European, Japanese, Asia Pacific ex Japan and North American (3 and 5 factors, monthly and daily) and Emerging markets (5 factors and momentum). US factors: daily from July 1, 1926, weekly from July 2, 1926 (3 factors and 6 size/value portfolios only), monthly from July 1926, all through September 2026; annual returns 1927 to 2025 are included in the monthly files (checked Oct 28, 2026).

Good for

  • Factor-model backtests and alpha regressions (Fama/French 3- and 5-factor, momentum)
  • Replicating published asset-pricing research with the authors' own factor files
  • Benchmarking a strategy against size, value, profitability and investment portfolios

Verdict

Essential resource for academic research and factor investing backtests. Gold standard for replicating published finance research. Since the 2025 data-method change (see Overview), the library rebuilds its full return history each month when it updates, so past values can change. The one-month T-bill rate (RF) comes from ICE BofA from June 2024 (Ibbotson before). No API: download the files, or use pandas-datareader's famafrench reader in Python.

Get the free weekly email

Free weekly email of newly verified free data. Unsubscribe by emailing us.

By entering your email and clicking Subscribe, you agree to receive the Signal Lab weekly email from Quantum Data Technologies Ltd., 3200-650 West Georgia Street, Vancouver, BC V6B 4P7, Canada (signal-lab@qdt.ai). You can unsubscribe at any time: reply "unsubscribe" to any email or write to signal-lab@qdt.ai. See our Privacy Policy.

Use in your agent

Load Kenneth French US factors into pandas and Parquet. Checked against the provider's documentation on Oct 29, 2026.

Raw skill file (/skills/kenneth-french-data-library.md)Also in our MCP server: call get_ingestion_skill with {"slug": "kenneth-french-data-library"}.

Access

  • No account or API key. Download the ZIP file directly.
  • No reuse license is stated; the library carries a copyright notice.

Request

bash
curl -O https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip

Format

ZIP containing one CSV text file. It has prose, a monthly table, a blank line, then an annual table; factor returns are percentages.

Load into pandas and Parquet

Needs Python 3 with requests, pandas and pyarrow.

python
"""Kenneth French US three factors (monthly) -> pandas -> Parquet. No key."""
import io
import zipfile
import requests
import pandas as pd

URL = "https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip"
r = requests.get(URL, timeout=60)
r.raise_for_status()
with zipfile.ZipFile(io.BytesIO(r.content)) as archive:
    text = archive.read(archive.namelist()[0]).decode("utf-8")
lines = text.splitlines()
header = next(i for i, line in enumerate(lines) if line.startswith(",Mkt-RF"))
end = next(i for i in range(header + 1, len(lines)) if not lines[i].strip())
df = pd.read_csv(io.StringIO("\n".join(lines[header:end])))
df = df.rename(columns={df.columns[0]: "month", "Mkt-RF": "mkt_rf"})
df["month"] = pd.to_datetime(df["month"].astype(str), format="%Y%m")
for column in ("mkt_rf", "SMB", "HML", "RF"):
    df[column] = pd.to_numeric(df[column]) / 100
df.to_parquet("kenneth_french_us_3_factors_monthly.parquet", index=False)
print(f"{len(df):,}", "monthly rows,", df["month"].min().date(), "to", df["month"].max().date())

Run by us on 2026-10-29 (python 3.13.5 | pandas 3.1.0 | pyarrow 26.0.0 | requests 2.34.2). Result: 1,203 monthly rows, 1926-07-01 to 2026-09-01.

Limits

  • Rate limit: Not stated.

Quirks

  • The file is not a single ordinary CSV table; isolate the monthly block before parsing.
  • From the January 2025 release, US returns use CRSP CIZ files and monthly returns compound daily returns with dividends reinvested on ex-dates.
  • The full history is reconstructed each month and can change.
  • Divide factor values by 100 before treating them as decimal returns.

Terms and attribution

  • No reuse license is stated. The page says Copyright Eugene F. Fama and Kenneth R. French.
  • Confirm permission for redistribution or commercial reuse before publishing a copy.

Sources

Information and education only. Not financial, investment, legal or tax advice. Listings can be inaccurate, incomplete or out of date and are provided "as is". Free tiers, limits and licenses belong to each provider and can change, so check the provider's own terms before you rely on them. Terms & Disclaimer

Related entries