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