This repository contains an R package that collects helper functions for developers and researchers familiar with Tidy Finance with R. The functions provide shortcuts to selected issues that the book discusses in detail.
You can install the released version of tidyfinance from CRAN
via:
install.packages("tidyfinance")You can install the development version of tidyfinance
from GitHub
via:
# install.packages("pak")
pak::pak("tidy-finance/r-tidyfinance")Load the package:
library(tidyfinance)The main functionality of the tidyfinance package
centers around data download. You can download most of the data that we
used in Tidy Finance
with R using the download_data() function or its
children.
The function always requires a domain argument and
depending on the domain typically also a dataset. For
instance, to download monthly Fama-French factors, you have to provide
the dataset name according to Ken
French’s Data Library:
download_data(
domain = "Fama-French",
dataset = "Fama/French 5 Factors (2x3) [Daily]",
start_date = "2000-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 5,284 × 7
#> date mkt_excess smb hml rmw cma risk_free
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2000-01-03 -0.0071 -0.0009 -0.0131 -0.0148 -0.007 0.0002
#> 2 2000-01-04 -0.0406 0.0034 0.0207 0.0053 0.0136 0.0002
#> 3 2000-01-05 -0.0009 0.0036 -0.0005 0.0045 0.0115 0.0002
#> 4 2000-01-06 -0.0074 -0.0004 0.0124 0.0064 0.0121 0.0002
#> 5 2000-01-07 0.0321 -0.0089 -0.0157 -0.0083 -0.01 0.0002
#> 6 2000-01-10 0.0175 0.0043 -0.0135 -0.022 -0.0024 0.0002
#> 7 2000-01-11 -0.0171 0.0033 0.0091 0.009 0.012 0.0002
#> 8 2000-01-12 -0.0069 -0.0022 0.0074 0.0042 0.0085 0.0002
#> 9 2000-01-13 0.0159 0.0047 -0.0084 -0.0172 -0.0103 0.0002
#> 10 2000-01-14 0.0114 0.0022 -0.0048 -0.0034 -0.006 0.0002
#> # ℹ 5,274 more rowsFor q factors, you provide the relevant file name:
download_data(
domain = "Global Q",
dataset = "q5_factors_monthly_2024",
start_date = "2000-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 252 × 7
#> date risk_free mkt_excess me ia roe eg
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2000-01-01 0.00434 -0.0476 0.0562 0.0294 -0.0500 0.00638
#> 2 2000-02-01 0.00430 0.0246 0.221 -0.0539 -0.0755 -0.0311
#> 3 2000-03-01 0.00438 0.0523 -0.144 0.0183 0.0767 0.0356
#> 4 2000-04-01 0.00460 -0.0638 -0.0486 0.0379 0.0871 0.0510
#> 5 2000-05-01 0.00478 -0.0437 -0.0460 0.00508 0.000196 0.0370
#> 6 2000-06-01 0.00374 0.0466 0.103 -0.0338 -0.0479 -0.0172
#> 7 2000-07-01 0.00477 -0.0242 0.00995 0.0225 0.0343 0.0329
#> 8 2000-08-01 0.00500 0.0703 0.00449 -0.00800 -0.0208 -0.0101
#> 9 2000-09-01 0.00509 -0.0543 0.0158 0.0522 0.0209 0.00734
#> 10 2000-10-01 0.00522 -0.0274 -0.0222 0.0292 0.0734 0.0656
#> # ℹ 242 more rowsTo download the Welch and Goyal (2008) macroeconomic predictors for monthly, quarterly, or annual frequency:
download_data(
domain = "Goyal-Welch",
dataset = "monthly",
start_date = "2000-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 252 × 15
#> date rp_div dp dy ep de svar bm ntis tbl
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2000-01-01 -0.0244 -4.42 -4.48 -3.35 -1.08 0.00521 0.155 0.0254 0.0532
#> 2 2000-02-01 0.0866 -4.40 -4.42 -3.31 -1.09 0.00300 0.167 0.0274 0.0555
#> 3 2000-03-01 -0.0355 -4.49 -4.40 -3.38 -1.11 0.00668 0.150 0.0183 0.0569
#> 4 2000-04-01 -0.0269 -4.46 -4.49 -3.34 -1.12 0.00794 0.153 0.0119 0.0566
#> 5 2000-05-01 0.0194 -4.44 -4.46 -3.32 -1.13 0.00519 0.156 0.00918 0.0579
#> 6 2000-06-01 -0.0212 -4.47 -4.44 -3.33 -1.13 0.00236 0.157 0.00712 0.0569
#> 7 2000-07-01 0.0532 -4.46 -4.47 -3.30 -1.15 0.00207 0.156 0.00471 0.0596
#> 8 2000-08-01 -0.0595 -4.52 -4.46 -3.35 -1.17 0.00104 0.146 0.00467 0.0609
#> 9 2000-09-01 -0.0105 -4.48 -4.53 -3.29 -1.19 0.00160 0.154 0.00447 0.06
#> 10 2000-10-01 -0.0876 -4.47 -4.48 -3.30 -1.17 0.00557 0.149 0.00374 0.0611
#> # ℹ 242 more rows
#> # ℹ 5 more variables: lty <dbl>, ltr <dbl>, tms <dbl>, dfy <dbl>, infl <dbl>To download data from Open Source Asset Pricing (OSAP):
download_data(
domain = "Open Source Asset Pricing",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 12 × 213
#> date am aop abnormal_accruals accruals accruals_bm activism1
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-01 -0.0790 -0.0255 0.0156 -1.40e-2 0.0567 NA
#> 2 2020-02-01 -0.0444 -0.0145 -0.0139 -1.11e-2 -0.0323 NA
#> 3 2020-03-01 -0.136 0.00547 -0.0361 1.74e-2 -0.0118 NA
#> 4 2020-04-01 -0.0333 -0.0113 0.0778 4.53e-2 -0.0238 NA
#> 5 2020-05-01 -0.117 0.0185 0.0207 -3.61e-3 -0.0268 NA
#> 6 2020-06-01 0.0165 -0.00604 0.0330 8.51e-5 0.0400 NA
#> 7 2020-07-01 -0.0366 -0.0913 -0.0209 3.00e-2 -0.0533 NA
#> 8 2020-08-01 0.0265 0.0191 -0.0249 9.04e-4 0.0223 NA
#> 9 2020-09-01 -0.0366 -0.0189 -0.0235 -1.53e-2 -0.0460 NA
#> 10 2020-10-01 0.0711 0.0304 -0.00682 -4.40e-3 0.0332 NA
#> 11 2020-11-01 0.0739 0.0202 0.0261 2.42e-2 0.193 NA
#> 12 2020-12-01 0.0148 0.000180 -0.00594 2.04e-2 -0.0292 NA
#> # ℹ 206 more variables: activism2 <dbl>, ad_exp <dbl>, age_ipo <dbl>,
#> # analyst_revision <dbl>, analyst_value <dbl>, announcement_return <dbl>,
#> # asset_growth <dbl>, bm <dbl>, bmdec <dbl>, bpebm <dbl>, beta <dbl>,
#> # beta_fp <dbl>, beta_liquidity_ps <dbl>, beta_tail_risk <dbl>,
#> # bid_ask_spread <dbl>, book_leverage <dbl>, brand_invest <dbl>,
#> # cboper_prof <dbl>, cf <dbl>, cpvol_spread <dbl>, cash <dbl>,
#> # cash_prod <dbl>, ch_asset_turnover <dbl>, ch_eq <dbl>, …To download characteristic-managed portfolio (factor) returns from Global Factor Data, select a
region, the factor content (a single factor, a theme,
"all_themes", or "all_factors"), a frequency,
and a weighting scheme:
download_data(
domain = "Global Factor Data",
region = "usa",
factors = "all_factors",
frequency = "monthly",
weighting = "vw_cap",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 1,836 × 9
#> location name freq weighting direction n_stocks n_stocks_min date
#> <chr> <chr> <chr> <chr> <int> <int> <int> <date>
#> 1 usa age monthly vw_cap -1 2858 1082 2020-01-01
#> 2 usa age monthly vw_cap -1 2863 1080 2020-02-01
#> 3 usa age monthly vw_cap -1 2864 1078 2020-03-01
#> 4 usa age monthly vw_cap -1 2800 1077 2020-04-01
#> 5 usa age monthly vw_cap -1 2800 1072 2020-05-01
#> 6 usa age monthly vw_cap -1 2846 1116 2020-06-01
#> 7 usa age monthly vw_cap -1 2801 1112 2020-07-01
#> 8 usa age monthly vw_cap -1 2820 1109 2020-08-01
#> 9 usa age monthly vw_cap -1 2851 1107 2020-09-01
#> 10 usa age monthly vw_cap -1 2806 1106 2020-10-01
#> # ℹ 1,826 more rows
#> # ℹ 1 more variable: ret <dbl>The dataset argument also gives access to the underlying
long-short portfolios ("portfolios"), industry returns
("industry"), and the reference files
"nyse_cutoffs" and "return_cutoffs":
download_data(
domain = "Global Factor Data",
dataset = "industry",
region = "usa",
classification = "gics",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 132 × 7
#> gics date n location ret freq weighting
#> <int> <date> <int> <chr> <dbl> <chr> <chr>
#> 1 30 2020-01-01 151 usa -0.0190 monthly vw_cap
#> 2 45 2020-01-01 473 usa 0.0277 monthly vw_cap
#> 3 10 2020-01-01 227 usa -0.143 monthly vw_cap
#> 4 35 2020-01-01 836 usa -0.0156 monthly vw_cap
#> 5 60 2020-01-01 40 usa -0.0148 monthly vw_cap
#> 6 25 2020-01-01 444 usa -0.0228 monthly vw_cap
#> 7 15 2020-01-01 217 usa -0.0631 monthly vw_cap
#> 8 50 2020-01-01 135 usa -0.0178 monthly vw_cap
#> 9 20 2020-01-01 458 usa -0.00683 monthly vw_cap
#> 10 55 2020-01-01 65 usa 0.0443 monthly vw_cap
#> # ℹ 122 more rowsUse list_supported_jkp_factors() to see the available
regions, or list_supported_jkp_factors("usa") to see the
factors available for a region.
To download the liquidity factors of Pastor and Stambaugh (2003) from Lubos Pastor’s data library:
download_data(
domain = "Pastor-Stambaugh",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 12 × 4
#> date agg_liq innov_liq traded_liq
#> <date> <dbl> <dbl> <dbl>
#> 1 2020-01-01 -0.0408 -0.0138 0.0191
#> 2 2020-02-01 0.00627 0.0236 0.0411
#> 3 2020-03-01 -0.260 -0.164 -0.125
#> 4 2020-04-01 0.0115 -0.0266 0.123
#> 5 2020-05-01 -0.0559 -0.0665 0.0691
#> 6 2020-06-01 -0.0151 0.0156 0.0661
#> 7 2020-07-01 0.0345 0.0742 0.000783
#> 8 2020-08-01 0.00611 -0.0116 0.0426
#> 9 2020-09-01 -0.0996 -0.123 -0.000901
#> 10 2020-10-01 -0.0431 0.0285 0.0392
#> 11 2020-11-01 0.0539 0.0689 0.0276
#> 12 2020-12-01 0.0249 0.0131 0.0545To download the mispricing factors of Stambaugh and Yuan (2017) from
Robert
Stambaugh’s data library, optionally selecting
"monthly" (the default) or "daily" data. Note
that the source files currently end in December 2016:
download_data(
domain = "Stambaugh-Yuan",
dataset = "monthly",
start_date = "2015-01-01",
end_date = "2016-12-31"
)
#> # A tibble: 24 × 6
#> date mkt_excess smb mgmt perf risk_free
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2015-01-01 -0.0311 -0.0271 -0.0227 0.0506 0
#> 2 2015-02-01 0.0613 0.0245 -0.0124 -0.0300 0
#> 3 2015-03-01 -0.0112 0.0214 -0.0192 0.00596 0
#> 4 2015-04-01 0.0059 -0.0260 0.00818 -0.0418 0
#> 5 2015-05-01 0.0136 0.0141 -0.00498 0.0108 0
#> 6 2015-06-01 -0.0153 0.0301 -0.00718 0.00387 0
#> 7 2015-07-01 0.0154 -0.0340 0.00419 0.0706 0
#> 8 2015-08-01 -0.0604 -0.00684 0.00725 0.0133 0
#> 9 2015-09-01 -0.0308 -0.0228 0.0373 0.0790 0
#> 10 2015-10-01 0.0775 -0.0227 0.0240 -0.00612 0
#> # ℹ 14 more rowsTo download multiple series from the Federal Reserve Economic Data (FRED):
download_data(
domain = "FRED",
series = c("GDP", "CPIAUCNS"),
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 16 × 3
#> date value series
#> <date> <dbl> <chr>
#> 1 2020-01-01 21751. GDP
#> 2 2020-04-01 19958. GDP
#> 3 2020-07-01 21704. GDP
#> 4 2020-10-01 22087. GDP
#> 5 2020-01-01 258. CPIAUCNS
#> 6 2020-02-01 259. CPIAUCNS
#> 7 2020-03-01 258. CPIAUCNS
#> 8 2020-04-01 256. CPIAUCNS
#> 9 2020-05-01 256. CPIAUCNS
#> 10 2020-06-01 258. CPIAUCNS
#> 11 2020-07-01 259. CPIAUCNS
#> 12 2020-08-01 260. CPIAUCNS
#> 13 2020-09-01 260. CPIAUCNS
#> 14 2020-10-01 260. CPIAUCNS
#> 15 2020-11-01 260. CPIAUCNS
#> 16 2020-12-01 260. CPIAUCNSTo download stock prices from Yahoo Finance:
download_data(
domain = "Stock Prices",
symbols = c("AAPL", "MSFT"),
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 504 × 8
#> symbol date volume open low high close adjusted_close
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2020-01-02 135480400 74.1 73.8 75.2 75.1 72.3
#> 2 AAPL 2020-01-03 146322800 74.3 74.1 75.1 74.4 71.6
#> 3 AAPL 2020-01-06 118387200 73.4 73.2 75.0 74.9 72.2
#> 4 AAPL 2020-01-07 108872000 75.0 74.4 75.2 74.6 71.9
#> 5 AAPL 2020-01-08 132079200 74.3 74.3 76.1 75.8 73.0
#> 6 AAPL 2020-01-09 170108400 76.8 76.6 77.6 77.4 74.6
#> 7 AAPL 2020-01-10 140644800 77.7 77.1 78.2 77.6 74.7
#> 8 AAPL 2020-01-13 121532000 77.9 77.8 79.3 79.2 76.3
#> 9 AAPL 2020-01-14 161954400 79.2 78.0 79.4 78.2 75.3
#> 10 AAPL 2020-01-15 121923600 78.0 77.4 78.9 77.8 75.0
#> # ℹ 494 more rowsYou can also download high-frequency data for the S&P 500 that we host on Hugging Face:
download_data(
domain = "Tidy Finance",
dataset = "high_frequency_sp500",
start_date = "2007-07-26",
end_date = "2007-07-27"
)
#> # A tibble: 9,360 × 9
#> ts midquote signed_volume trading_volume depth0_ask
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-07-26 09:30:05 150. 0 11565577. 13841.
#> 2 2007-07-26 09:30:10 150. 0 3017263. 10795.
#> 3 2007-07-26 09:30:15 150. 0 6108290. 12371.
#> 4 2007-07-26 09:30:20 150. 98630 21820258. 14069.
#> 5 2007-07-26 09:30:25 150. 0 931698 15283.
#> 6 2007-07-26 09:30:30 150. -4400 661077 5307.
#> 7 2007-07-26 09:30:35 150. -12000 2102820 3192.
#> 8 2007-07-26 09:30:40 150. 39927 12384621. 6234.
#> 9 2007-07-26 09:30:45 150. -14671 3105370. 9255.
#> 10 2007-07-26 09:30:50 150. 5347 5518370. 5708.
#> # ℹ 9,350 more rows
#> # ℹ 4 more variables: depth0_bid <dbl>, depth5_ask <dbl>, depth5_bid <dbl>,
#> # spread <dbl>You can also download data directly from WRDS if you have access to
the underlying data and set your credentials via
Sys.setenv(WRDS_USER = "your_username", WRDS_PASSWORD = "your_password")
or the set_wrds_credentials() helper function.
To download monthly CRSP data:
download_data(
domain = "WRDS",
dataset = "crsp_monthly",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 43,329 × 14
#> permno date calculation_date ret shrout prc primaryexch siccd
#> <int> <date> <date> <dbl> <dbl> <dbl> <chr> <int>
#> 1 10026 2020-01-01 2020-01-31 -0.100 1.89e7 166. Q 2052
#> 2 10028 2020-01-01 2020-01-31 0.607 2.69e7 2.17 A 5094
#> 3 10032 2020-01-01 2020-01-31 -0.0756 2.92e7 71.1 Q 3672
#> 4 10044 2020-01-01 2020-01-31 -0.0986 6 e6 8.32 Q 2060
#> 5 10051 2020-01-01 2020-01-31 -0.115 3.73e7 24.4 N 8093
#> 6 10104 2020-01-01 2020-01-31 -0.00561 3.21e9 52.4 N 7372
#> 7 10107 2020-01-01 2020-01-31 0.0795 7.61e9 170. Q 7370
#> 8 10138 2020-01-01 2020-01-31 0.0959 2.35e8 134. Q 6211
#> 9 10145 2020-01-01 2020-01-31 -0.0214 7.15e8 173. N 5099
#> 10 10158 2020-01-01 2020-01-31 0.0966 2.90e7 19.2 N 8711
#> # ℹ 43,319 more rows
#> # ℹ 6 more variables: listing_age <int>, mktcap <dbl>, mktcap_lag <dbl>,
#> # exchange <chr>, industry <chr>, ret_excess <dbl>To download annual (or quaterly) Compustat data:
download_data(
domain = "WRDS",
dataset = "compustat_annual",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> # A tibble: 11,990 × 25
#> gvkey date datadate seq ceq at lt txditc txdb
#> <chr> <date> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 001004 2020-05-01 2020-05-31 903. 9.03e2 2.08e+3 1.18e+3 0 0
#> 2 001019 2020-12-01 2020-12-31 13.5 1.35e1 4.06e+1 2.71e+1 3.61e-1 3.61e-1
#> 3 001045 2020-12-01 2020-12-31 -6867 -6.87e3 6.20e+4 6.89e+4 9 e+0 9 e+0
#> 4 001050 2020-12-01 2020-12-31 203. 2.03e2 4.19e+2 2.16e+2 6.97e+0 6.97e+0
#> 5 001062 2020-11-01 2020-11-30 464. 4.64e2 4.65e+2 8.04e-1 0 0
#> 6 001075 2020-12-01 2020-12-31 5634. 5.63e3 2.00e+4 1.43e+4 2.33e+3 2.14e+3
#> 7 001076 2020-12-01 2020-12-31 986. 9.86e2 1.32e+3 3.31e+2 1.27e+2 1.27e+2
#> 8 001078 2020-12-01 2020-12-31 32784 3.28e4 7.25e+4 3.95e+4 1.41e+3 1.41e+3
#> 9 001084 2020-12-01 2020-12-31 -2.67 -2.67e0 6.92e-1 3.36e+0 0 0
#> 10 001096 2020-12-01 2020-12-31 3372. 3.37e3 1.11e+4 6.69e+3 6.48e+2 6.48e+2
#> # ℹ 11,980 more rows
#> # ℹ 16 more variables: itcb <dbl>, pstkrv <dbl>, pstkl <dbl>, pstk <dbl>,
#> # capx <dbl>, oancf <dbl>, sale <dbl>, cogs <dbl>, xint <dbl>, xsga <dbl>,
#> # ib <dbl>, curcd <chr>, be <dbl>, op <dbl>, at_lag <dbl>, inv <dbl>To download the CRSP-Compustat linking table:
download_data(
domain = "WRDS",
dataset = "ccm_links"
)
#> # A tibble: 33,324 × 4
#> permno gvkey linkdt linkenddt
#> <dbl> <chr> <date> <date>
#> 1 25881 001000 1970-11-13 1978-06-30
#> 2 10015 001001 1983-09-20 1986-07-31
#> 3 10023 001002 1972-12-14 1973-06-05
#> 4 10031 001003 1983-12-07 1989-08-16
#> 5 54594 001004 1972-04-24 2026-07-02
#> 6 61903 001005 1973-01-31 1983-01-31
#> 7 10058 001007 1973-10-01 1979-01-30
#> 8 10058 001007 1979-01-31 1984-09-28
#> 9 10066 001008 1983-08-25 1987-02-26
#> 10 10074 001009 1982-01-18 1996-03-13
#> # ℹ 33,314 more rowsTo download Enhanced TRACE data for selected bonds:
download_data(
domain = "WRDS",
dataset = "trace_enhanced",
cusips = c("00101JAH9"),
start_date = "2019-01-01",
end_date = "2021-12-31"
)
#> # A tibble: 7,694 × 8
#> cusip_id trd_exctn_dt trd_exctn_tm rptd_pr entrd_vol_qt yld_pt rpt_side_cd
#> <chr> <date> <time> <dbl> <dbl> <dbl> <chr>
#> 1 00101JAH9 2019-01-02 09:22:14 92.3 5000 6.12 S
#> 2 00101JAH9 2019-01-02 09:22:14 92.3 5000 6.12 S
#> 3 00101JAH9 2019-01-02 12:14:09 91 3314000 6.49 B
#> 4 00101JAH9 2019-01-02 14:01:00 91.2 5000 6.43 S
#> 5 00101JAH9 2019-01-02 14:01:00 91.3 24000 6.41 S
#> 6 00101JAH9 2019-01-02 14:01:00 91.3 5000 6.41 S
#> 7 00101JAH9 2019-01-02 14:01:03 91.2 24000 6.43 S
#> 8 00101JAH9 2019-01-02 15:50:07 91.8 315000 6.28 S
#> 9 00101JAH9 2019-01-03 09:17:16 91.5 50000 6.34 S
#> 10 00101JAH9 2019-01-03 09:17:16 91.5 50000 6.34 S
#> # ℹ 7,684 more rows
#> # ℹ 1 more variable: cntra_mp_id <chr>You can download pre-computed portfolio returns from the Tidy Finance Factor Library hosted on Hugging Face:
download_data(
domain = "Tidy Finance",
dataset = "factor_library",
sorting_variable = "ag"
)
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A data frame: 779 × 17
#> id date ret_type ret sorting_variable min_size_quantile
#> <int> <date> <chr> <dbl> <chr> <dbl>
#> 1 297554 1960-02-01 vw 0 ag 0.2
#> 2 297554 1960-03-01 vw 0 ag 0.2
#> 3 297554 1960-04-01 vw 0 ag 0.2
#> 4 297554 1960-05-01 vw 0 ag 0.2
#> 5 297554 1960-06-01 vw 0 ag 0.2
#> 6 297554 1960-07-01 vw 0 ag 0.2
#> 7 297554 1960-08-01 vw 0 ag 0.2
#> 8 297554 1960-09-01 vw 0 ag 0.2
#> 9 297554 1960-10-01 vw 0 ag 0.2
#> 10 297554 1960-11-01 vw 0 ag 0.2
#> # ℹ 769 more rows
#> # ℹ 11 more variables: exclude_financials <lgl>, exclude_utilities <lgl>,
#> # exclude_negative_earnings <lgl>, sorting_variable_lag <chr>,
#> # rebalancing <chr>, n_portfolios_main <chr>, sorting_method <chr>,
#> # breakpoints_min_size_threshold <dbl>, n_portfolios_secondary <dbl>,
#> # breakpoints_exchanges <chr>, weighting_scheme <chr>The package also provides functions to construct your own factor portfolios:
# Portfolio sorts and long-short returns
compute_portfolio_returns()
compute_long_short_returns()
# Configuration helpers
breakpoint_options()
data_options()
# Rolling and lagging
add_lagged_columns()
join_lagged_values()
compute_rolling_value()
# Regression estimation
estimate_model()
estimate_betas()
estimate_fama_macbeth()We include functions to check out content from tidy-finance.org:
list_tidy_finance_chapters()
open_tidy_finance_website()There are also some simple helpers for regression analyses:
winsorize()
trim()
create_summary_statistics()