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