FamaFrenchData.jl is a Julia package providing access to the Fama-French data available on the Ken French Data Library. Working with the data is sometimes tedious because the downloadable files come (1) compressed in zip archives and (2) having non-standard csv layouts. This package handles both of those hurdles, allowing users to get to the data faster.


To add the package, type ] add FamaFrenchData at the Julia REPL.

Once added, type using FamaFrenchData to import the package.

The package exports 3 functions: readFamaFrench, downloadFamaFrench, and listFamaFrench.

For help with any of these functions, use ? at the REPL (eg. ?readFamaFrench).


The Fama-French 3 factor model is a commonly used empirical asset pricing model. This example retrieves the full time series of FF3 monthly and annual returns.

using FamaFrenchData, DataFrames

tables, tablenotes, filenotes = readFamaFrench("F-F_Research_Data_Factors")

FF3_monthly = tables[1]
FF3_annual = tables[2]

If you would rather store the file locally (perhaps so your code is reproducible later on), use downloadFamaFrench.

using FamaFrenchData, DataFrames

savename = "path/to/my/file.csv"
tables, tablenotes, filenotes = readFamaFrench(savename) # also reads local files

To get a list of all of the available table names, use listFamaFrench.

using FamaFrenchData
FFnames = listFamaFrench()

Additional Notes

  • Original files use -99.99 or -999 to encode missing values, I attempt to replace these with missing.
  • Original files have no column heading for column 1, I name this column :Date. It maintains the parsed type of Int because it can take several different forms: 20011231,200112,2001.
  • I have not verified that all tables are parsed correctly.
  • Breakpoints files require the keyword argument header=false


I am not affiliated with the Ken French Data Library. This package does not "ship with" the data, just provides easier access to it. Other than the changes that I have explicitly stated, I do not alter the data; however, it is your responsibility to verify that the data is correct.


FamaFrenchData.readFamaFrench โ€” Function

ffn can be the table name (in which case it is retreived from the web) or a path to the local file. kwargs are passed to CSV.File. Missing values (-99.99 or -999) are replaced with missing.

Returns three pieces:

- `tables::Vector{DataFrame}` - the extracted tables

- `tablenotes::Vector{String}` - any notes to the tables

- `filenotes::String` - notes at the top of the file

Example Usage:

using DataFrames, FamaFrenchData

# read the Fama-French 3 factors (monthly and annual)
tables, tablenotes, filenotes = readFamaFrench("F-F_Research_Data_Factors")

# read the Fama-French 3 factors (daily)
tablesd, tablenotesd, filenotesd = readFamaFrench("F-F_Research_Data_Factors_Daily")

# read the 25 Size-B/M portfolios (monthly and annual)
tables25, tablenotes25, filenotes25 = readFamaFrench("25_Portfolios_5x5")
FamaFrenchData.listFamaFrench โ€” Function

Returns a vector of possible table names. Reads from listFamaFrench.txt. When refresh = true, first crawls the website to find current list of tables, then overwrites listFamaFrench.txt with this list. The selection of tables is rarely changed, so the provided list is likely sufficient.