At the time of writing, the analytics part of the pipeline in finRes focusses on asset pricing.
FFresearch
The FFresearch package provides data on classic asset pricing factors and a number of sort portfolios. The data is pulled directly from Kenneth French’s data library and tidied up for seamless consumption in R.
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 0.47bivariate
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:
#> 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 0.23trivariate
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 0.7549industries
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 -1.92factors
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 very 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 -1.74variables
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 above-mentioned:
#> 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 1466.86factors
Meanwhile, the factors package provides various asset pricing research factor time series for convenient consumption in R with the data directly pulled from the authors’ website. The current version includes the factor data from Kenneth’s French, also available in the FFresearch package described above, as well as factor data from Robert F. Stambaugh.
Fama & French
The fama_french 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 very 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 -1.74Stambaugh et al
The stambaugh dataset provides the return (factors) and level (risk free rate) time series for various research asset pricing factors put together by Robert F. Stambaugh and collaborators including Lubos Pastor and Yu Yuan. The factors include traded & non-traded liquidity (Pástor and Stambaugh 2003), as well as market, size and two ‘mispricing’ factors: management & performance (Stambaugh and Yuan 2016):
#> frequency factor period value
#> 1: month non-traded liquidity 196208 0.00426023
#> 2: month non-traded liquidity 196209 0.01172080
#> 3: month non-traded liquidity 196210 -0.07442466
#> 4: month non-traded liquidity 196211 0.02854555
#> 5: month non-traded liquidity 196212 0.01435009
#> 6: month non-traded liquidity 196301 0.00947839factor’em!
factorem on the other hand provides tools for outright factor construction from data retrieved with pullit that facilitate asset pricing research and factor investment backtesting. The returned objects carry corresponding return & positions time series and a number of methods help with performance analysis. The package is organised around a workhorse function and a series of wrappers for asset pricing factors popular in the literature. The latter functions get raw financial data retrieved from Bloomberg using the pullit package and return S4 objects that carry data belonging to the corresponding factor including positions and return time series.
factorem
The factorem function is the workhorse function in factorem. From raw financial data and a set of parameter specifications it constructs a complete backtest of the corresponding asset pricing factor and returns the whole time series for the factor positions and return. For the record the S4 object returned by the function also contains the original raw financial and the set of parameters supplied as well as the original call to the function. The raw financial data should be supplied in a format similar to that of historical datasets returned returned by historical data query functions in pullit:
library(pullit); library(lubridate)
tickers_equity <- c("LZB US Equity", "SGA US Equity", "AGCO US Equity", "CLR US Equity",
"GHC US Equity", "MAN US Equity", "SITE US Equity", "AJRD US Equity",
"COMM US Equity", "GME US Equity", "MEI US Equity", "SMP US Equity")
start <- "2016-01-01"; end <- "2017-12-31"
equity_data_market <- pull_equity_market(source = "Bloomberg", tickers_equity, start, end, verbose = F)
get_data(equity_data_market)#> ticker field date value
#> 1: AGCO US Equity CUR_MKT_CAP 2016-11-21 4271.793
#> 2: AGCO US Equity CUR_MKT_CAP 2016-11-22 4360.889
#> 3: AGCO US Equity CUR_MKT_CAP 2016-11-23 4507.777
#> 4: AGCO US Equity CUR_MKT_CAP 2016-11-25 4535.067
#> 5: AGCO US Equity CUR_MKT_CAP 2016-11-28 4518.211
#> ---
#> 33466: SMP US Equity PX_VOLUME 2017-12-22 89626.000
#> 33467: SMP US Equity PX_VOLUME 2017-12-26 48762.000
#> 33468: SMP US Equity PX_VOLUME 2017-12-27 119607.000
#> 33469: SMP US Equity PX_VOLUME 2017-12-28 81731.000
#> 33470: SMP US Equity PX_VOLUME 2017-12-29 153615.000From there, construct a bespoke asset pricing factor using the factorem function. I.e., an equity momentum factor:
library(factorem)
ranking_period = 1L
factor <- factorem(
name = "momentum", data = pullit::get_data(equity_data_market),
sort_levels = FALSE, weighted = FALSE, ranking_period = ranking_period
)
factor
#> AssetPricingFactor
#> name: momentum factor
#> parameters
#> name: momentum
#> update frequency: month
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: PX_LAST
#> sort levels: FALSE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5wrappers
factorem provides wrapper methods for popular asset pricing factors in the literature. At the time of writing the factors covered in the package include, equity market and momentum as well as futures market, momentum, commercial hedging pressure (CHP), open interest and term structure factors. The author welcomes pull requests that would help expanding the current coverage.
equity
market
The equity market factor encompasses the entire cross-section of a defined investment opportunity set. It gained popularity in the 1960s for the central role it plays in the Capital Asset Pricing Model (Treynor 1961a, 1961b; Sharpe 1964; Mossin 1966; Lintner 1975) and remains the most popular factor to date in the literature when it comes to explaining the cross-section of equity returns:
equity_market <- market_factor(data = equity_data_market)
equity_market
#> MarketFactor
#> name: equity market factor
#> parameters
#> return frequency: month
#> long: TRUEmomentum
The equity momentum factor is another popular factor in the asset pricing literature. After being first introduced in Carhart (1997) it eventually drew the field leaders’ attention in Fama and French (2012) The equity momentum sorts on prior returns over a defined ranking period:
equity_momentum <- momentum_factor(data = equity_data_market, ranking_period = ranking_period)
equity_momentum
#> MomentumFactor
#> name: equity momentum factor
#> parameters
#> name: equity momentum
#> update frequency: week
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: PX_LAST
#> sort levels: FALSE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5futures
tickers_futures <- c(
"C A Comdty", "CLA Comdty", "COA Comdty", "GCA Comdty", "HOA Comdty",
"KWA Comdty", "LHA Comdty", "QSA Comdty", "S A Comdty", "SBA Comdty",
"W A Comdty"
)
futures_data_TS <- pull_futures_market(source = "Bloomberg", type = "term structure", tickers_futures,
start, end, verbose = F)
get_data(futures_data_TS)#> ticker field date value
#> 1: C 1 A:00_0_N Comdty OPEN_INT 2016-01-04 727298
#> 2: C 1 A:00_0_N Comdty OPEN_INT 2016-01-05 730600
#> 3: C 1 A:00_0_N Comdty OPEN_INT 2016-01-06 727995
#> 4: C 1 A:00_0_N Comdty OPEN_INT 2016-01-07 727244
#> 5: C 1 A:00_0_N Comdty OPEN_INT 2016-01-08 719406
#> ---
#> 239938: W 5 A:00_0_N Comdty PX_VOLUME 2017-12-22 4135
#> 239939: W 5 A:00_0_N Comdty PX_VOLUME 2017-12-26 2339
#> 239940: W 5 A:00_0_N Comdty PX_VOLUME 2017-12-27 1129
#> 239941: W 5 A:00_0_N Comdty PX_VOLUME 2017-12-28 1657
#> 239942: W 5 A:00_0_N Comdty PX_VOLUME 2017-12-29 1617market
factorem provides futures equivalent for the factors above including a futures market factor that takes equally weighted positions on the nearby front contract of the commodity futures series provided:
futures_market <- market_factor(data = futures_data_TS)
futures_market
#> MarketFactor
#> name: futures nearby market factor
#> parameters
#> return frequency: month
#> long: TRUEmomentum
The momentum factor is also popular in the futures asset pricing literature in particular in the context of commodity markets (Miffre and Rallis 2007). As does its equity equivalent, it sorts on prior returns:
ranking_period = 1L
futures_momentum <- momentum_factor(data = futures_data_TS, ranking_period = ranking_period)
futures_momentum
#> MomentumFactor
#> name: futures momentum factor
#> parameters
#> name: futures momentum
#> update frequency: week
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: PX_LAST
#> sort levels: FALSE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5commercial hedging pressure (CHP)
The futures commercial hedging pressure factor is based on the well-known hedging pressure-based theory (Keynes 1930; Hicks 1939; Houthakker 1957; Cootner 1960) which postulates that futures prices for a given commodity are inversely related to the extent that commercial hedgers are short or long and the mimicking portfolio here aims at capturing the impact of hedging pressure as a systemic factor (Basu and Miffre 2013).
futures_data_CFTC <- pull_futures_CFTC(source = "Bloomberg", tickers_futures, start, end, verbose = F)
ranking_period = 1L
futures_CHP <- CHP_factor(price_data = futures_data_TS, CHP_data = futures_data_CFTC,
ranking_period = ranking_period)
futures_CHP#> CHPFactor
#> name: CHP factor
#> parameters
#> name: CHP
#> update frequency: month
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: inverse CHP
#> sort levels: TRUE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5open interest
The open interest factor relates to futures market liquidity and is also present in the literature where it is described as having explanatory power on the cross-section of commodity futures returns (Hong and Yogo 2012). It comes in two flavours in factorem, nearby and aggregate.
nearby
The nearby open interest factor is concerned with liquidity at the very front end of the term structure where it sorts on nearby front contract’s open interest:
ranking_period = 1L
futures_OI_nearby <- OI_nearby_factor(data = futures_data_TS, ranking_period = ranking_period)
futures_OI_nearby
#> OIFactor
#> name: nearby OI factor
#> parameters
#> name: nearby OI
#> update frequency: month
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: OPEN_INT
#> sort levels: FALSE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5aggregate
In contrast, the aggregate open interest factor is concerned with term structure liquidity as a whole and sorts on open interest summed up over all the contracts of a particular futures series:
futures_data_agg <- pull_futures_market(source = "Bloomberg", type = "aggregate", tickers_futures,
start, end, verbose = F)
ranking_period = 1L
futures_OI_aggregate <- OI_aggregate_factor(price_data = futures_data_market,
aggregate_data = futures_data_agg,
ranking_period = ranking_period)
futures_OI_aggregate#> OIFactor
#> name: aggregate OI factor
#> parameters
#> name: aggregate OI
#> update frequency: month
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: FUT_AGGTE_OPEN_INT
#> sort levels: FALSE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5term structure
The futures term structure factor (Szymanowska et al. 2014; Fuertes, Miffre, and Fernandez-Perez 2015) is concerned with the shape (steepness) of the futures term structure and sorts on roll yield:
ranking_period = 1L
futures_TS <- TS_factor(data = futures_data_TS, ranking_period = ranking_period)
futures_TS
#> TSFactor
#> name: futures term structure factor
#> parameters
#> name: futures term structure
#> update frequency: month
#> return frequency: day
#> price variable: PX_LAST
#> sort variable: carry
#> sort levels: TRUE
#> weighted: FALSE
#> ranking period: 1
#> long threshold: 0.5
#> short threshold: 0.5accessors
Accessor methods allow access to the content of the factor objects returned by the functions described above: #### name
get_name(factor)
#> [1] "momentum"positions
get_positions(factor)
#> date name position
#> 1: 2016-12-30 LZB US Equity long
#> 2: 2016-12-30 MEI US Equity long
#> 3: 2016-12-30 SMP US Equity long
#> 4: 2016-12-30 SGA US Equity long
#> 5: 2016-12-30 GHC US Equity long
#> ---
#> 152: 2017-12-29 SGA US Equity short
#> 153: 2017-12-29 LZB US Equity short
#> 154: 2017-12-29 GME US Equity short
#> 155: 2017-12-29 GHC US Equity short
#> 156: 2017-12-29 MAN US Equity shortreturns
get_returns(factor)
#> date long short factor
#> 1: 2017-01-03 0.0135047148 -0.0109830009 0.0012608570
#> 2: 2017-01-04 0.0143650103 -0.0218710531 -0.0037530214
#> 3: 2017-01-05 -0.0324683966 0.0101188617 -0.0111747674
#> 4: 2017-01-06 0.0009696990 0.0112154077 0.0060925534
#> 5: 2017-01-09 -0.0068085180 0.0063287768 -0.0002398706
#> ---
#> 247: 2017-12-22 -0.0007898250 0.0101846620 0.0046974185
#> 248: 2017-12-26 0.0093618073 0.0063778750 0.0078698411
#> 249: 2017-12-27 -0.0001223505 -0.0012346003 -0.0006784754
#> 250: 2017-12-28 -0.0001494908 0.0008308516 0.0003406804
#> 251: 2017-12-29 -0.0059587926 0.0079990948 0.0010201511data initially supplied
factorem::get_data(factor)
#> ticker field date value
#> 1: AGCO US Equity CUR_MKT_CAP 2016-11-21 4271.793
#> 2: AGCO US Equity CUR_MKT_CAP 2016-11-22 4360.889
#> 3: AGCO US Equity CUR_MKT_CAP 2016-11-23 4507.777
#> 4: AGCO US Equity CUR_MKT_CAP 2016-11-25 4535.067
#> 5: AGCO US Equity CUR_MKT_CAP 2016-11-28 4518.211
#> ---
#> 33466: SMP US Equity PX_VOLUME 2017-12-22 89626.000
#> 33467: SMP US Equity PX_VOLUME 2017-12-26 48762.000
#> 33468: SMP US Equity PX_VOLUME 2017-12-27 119607.000
#> 33469: SMP US Equity PX_VOLUME 2017-12-28 81731.000
#> 33470: SMP US Equity PX_VOLUME 2017-12-29 153615.000parameters
get_parameters(factor)
#> parameter value
#> 1: name momentum
#> 2: update frequency month
#> 3: return frequency day
#> 4: price variable PX_LAST
#> 5: sort variable PX_LAST
#> 6: sort levels FALSE
#> 7: weighted FALSE
#> 8: ranking_period 1
#> 9: long_threshold 0.5
#> 10: short_threshold 0.5function call
get_call(factor)
#> factorem(name = "momentum", data = pullit::get_data(equity_data_market),
#> sort_levels = FALSE, weighted = FALSE, ranking_period = ranking_period)summary
A summary method returns a performance summary of the corresponding factor:
summary(factor)
#> year Jan Feb Mar Apr May Jun Jul Aug
#> 1 2017 -0.01362 0.01101 0.03168 -0.00643 -0.00806 -0.00909 -0.00299 -0.01587
#> Sep Oct Nov Dec total
#> 1 -0.00367 0.00101 0.04334 0.03185 0.05851plotit
The plotit package, also part of the finRes suite, provides plot methods for the factor objects returned by the functions above:
performance overview
The plot function dispatched on a factor object from factorem with the parameter “type” set to “performance” displays an overview of the performance of the corresponding factor in the form of equity curves. The equity curve for the factor is plotted along with, for long-short factors only, that of the long and short legs of the factor independently:
positions overview
Similarly, with the “type” parameter set to “positions”, the plot function shows the proportion of time each asset leaves in the factor with a breakdown by leg for long-short factors:
plot(object = factor, type = "positions")
references
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