FFresearch packages Fama/French research data for convenient consumption by R users. The data is pulled directly from Kenneth French’s online data library.
Install from github with devtools::install_github("bautheac/FFresearch").

Portfolios

Univariate

The portfolios_univariate dataset provides various feature time series for Fama/French portfolios formed on single variable sorts. Sorting variables include size, book-to-market, operating profitability and investment:

#>    region frequency         sort variable dividend weights portfolio  field
#> 1:     US       day market capitalization        Y   value     Dec 2 return
#> 2:     US       day market capitalization        Y   value     Dec 2 return
#> 3:     US       day market capitalization        Y   value     Dec 2 return
#> 4:     US       day market capitalization        Y   value     Dec 2 return
#> 5:     US       day market capitalization        Y   value     Dec 2 return
#> 6:     US       day market capitalization        Y   value     Dec 2 return
#>      period value
#> 1: 19710104 -0.29
#> 2: 19710105  1.65
#> 3: 19710106  1.37
#> 4: 19710107  0.11
#> 5: 19710108 -0.19
#> 6: 19710111  0.47

Bivariate

The portfolios_bivariate dataset provides various feature time series for Fama/French portfolios formed on two variable sorts. Sorting variables include size, book-to-market, operating profitability and investment. Size concerns limit the data history to the last ten years; the full time series are available from the author upon request.

#>    region frequency       sort variable 1 sort variable 2 dividend weights
#> 1:     US       day market capitalization     book/market        Y   value
#> 2:     US       day market capitalization     book/market        Y   value
#> 3:     US       day market capitalization     book/market        Y   value
#> 4:     US       day market capitalization     book/market        Y   value
#> 5:     US       day market capitalization     book/market        Y   value
#> 6:     US       day market capitalization     book/market        Y   value
#>    portfolio  field   period value
#> 1:  BIG HiBM return 20110103  4.81
#> 2:  BIG HiBM return 20110104  0.16
#> 3:  BIG HiBM return 20110105  1.80
#> 4:  BIG HiBM return 20110106 -0.40
#> 5:  BIG HiBM return 20110107 -0.71
#> 6:  BIG HiBM return 20110110  0.23

Trivariate

The portfolios_trivariate dataset provides various feature time series for Fama/French portfolios formed on three variable sorts. Sorting variables include size, book-to-market, operating profitability and investment:

#>    region frequency       sort variable 1 sort variable 2
#> 1:     US     month market capitalization     book/market
#> 2:     US     month market capitalization     book/market
#> 3:     US     month market capitalization     book/market
#> 4:     US     month market capitalization     book/market
#> 5:     US     month market capitalization     book/market
#> 6:     US     month market capitalization     book/market
#>            sort variable 3 dividend weights     portfolio  field period   value
#> 1: operating profitability        Y   value BIG HiBM.HiOP return 197101 18.7986
#> 2: operating profitability        Y   value BIG HiBM.HiOP return 197102  4.1366
#> 3: operating profitability        Y   value BIG HiBM.HiOP return 197103  0.6142
#> 4: operating profitability        Y   value BIG HiBM.HiOP return 197104  0.9330
#> 5: operating profitability        Y   value BIG HiBM.HiOP return 197105  2.6881
#> 6: operating profitability        Y   value BIG HiBM.HiOP return 197106  0.7549

Industries

The portfolios_industries dataset provides various feature time series for Fama/French industry portfolios (Fama and French 1997):

#>    region frequency dividend weights portfolio  field period value
#> 1:     US     month        Y   value      Aero return 197101 20.39
#> 2:     US     month        Y   value      Aero return 197102  4.36
#> 3:     US     month        Y   value      Aero return 197103  2.49
#> 4:     US     month        Y   value      Aero return 197104  6.54
#> 5:     US     month        Y   value      Aero return 197105 -4.19
#> 6:     US     month        Y   value      Aero return 197106 -1.92

Factors

The factors dataset provides the return (factors) and level (risk free rate) time series for the classic Fama/French asset pricing factors as used in their three (Fama and French 1992, 1993, 1995) and most recently five-factor (Fama and French 2015, 2016, 2017) asset pricing models extremely popular to the asset pricing enthusiasts:

#>    region frequency factor period value
#> 1:     US     month    CMA 197101 -0.14
#> 2:     US     month    CMA 197102 -0.72
#> 3:     US     month    CMA 197103 -2.69
#> 4:     US     month    CMA 197104  0.72
#> 5:     US     month    CMA 197105  0.30
#> 6:     US     month    CMA 197106 -1.74

Variables

The variables dataset is a helper dataset that provides details, including construction methods, for the variables used to construct the portfolios and asset pricing factors above:

#> # A tibble: 6 x 3
#>   name              symbol description                                          
#>   <chr>             <chr>  <chr>                                                
#> 1 market capitaliz… ME     Market equity (size) is price times shares outstandi…
#> 2 book value        BE     Book equity is constructed from Compustat data or co…
#> 3 book/market       ME/BE  The book-to-market ratio used to form portfolios in …
#> 4 operating profit… OP     The operating profitability ratio used to form portf…
#> 5 investment        INV    The investment ratio used to form portfolios in June…
#> 6 earnings/price    E/P    Earnings is total earnings before extraordinary item…

Breakpoints

The breakpoints dataset is a helper dataset that provides the times series for the variables breakpoints used to construct the variables that in turn allow the construction of the portfolios and asset pricing factors abovementioned:

#>    variable frequency percentile period   value
#> 1:     size     month # positive 202104 1142.00
#> 2:     size     month         5% 202104  191.41
#> 3:     size     month        10% 202104  469.18
#> 4:     size     month        15% 202104  689.71
#> 5:     size     month        20% 202104 1035.99
#> 6:     size     month        25% 202104 1466.86

finRes

Although the FFresearch package is self-contained it belongs to the finRes suite of packages where it helps with asset pricing research and analysis.

References

Fama, Eugene F., and Kenneth R. French. 1992. “The Cross-Section of Expected Stock Returns.” The Journal of Finance 47 (2): 427–65. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x.

———. 1993. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33 (1): 3–56. https://doi.org/10.1016/0304-405X(93)90023-5.

———. 1995. “Size and Book-to-Market Factors in Earnings and Returns.” The Journal of Finance 50 (1): 131–55. https://doi.org/10.1111/j.1540-6261.1995.tb05169.x.

———. 1997. “Industry Costs of Equity.” Journal of Financial Economics 43 (2): 153–93. https://doi.org/10.1016/S0304-405X(96)00896-3.

———. 2015. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics 116 (1): 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010.

———. 2016. “Dissecting Anomalies with a Five-Factor Model.” The Review of Financial Studies 29 (1): 69–103. https://doi.org/10.1093/rfs/hhv043.

———. 2017. “International Tests of a Five-Factor Asset Pricing Model.” Journal of Financial Economics 123 (3): 441–63. https://doi.org/10.1016/j.jfineco.2016.11.004.