tidyfinance

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.

Installation

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("remotes")
remotes::install_github("tidy-finance/r-tidyfinance")

Usage

Load the package:

library(tidyfinance)

Download Open Source Data

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 -0.0146 -0.0071    0.0002
#>  2 2000-01-04    -0.0406  0.0034  0.0207  0.0053  0.0136    0.0002
#>  3 2000-01-05    -0.0009  0.0035 -0.0005  0.0044  0.0114    0.0002
#>  4 2000-01-06    -0.0074 -0.0003  0.0124  0.0064  0.0122    0.0002
#>  5 2000-01-07     0.0321 -0.0089 -0.0157 -0.0083 -0.0099    0.0002
#>  6 2000-01-10     0.0175  0.0043 -0.0135 -0.022  -0.0024    0.0002
#>  7 2000-01-11    -0.0171  0.0034  0.0092  0.009   0.012     0.0002
#>  8 2000-01-12    -0.0069 -0.0022  0.0074  0.0041  0.0085    0.0002
#>  9 2000-01-13     0.0159  0.0048 -0.0084 -0.0172 -0.0102    0.0002
#> 10 2000-01-14     0.0114  0.0022 -0.0048 -0.0035 -0.0059    0.0002
#> # ℹ 5,274 more rows

For 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 rows

To 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 rows

Use 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 Robert Stambaugh’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   0.0545

To 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 rows

To download the cost of capital (WACC) by industry from Aswath Damodaran’s data library, optionally selecting a region (e.g., "us", the default, or "europe") and archived years via year. Reading the Excel files requires the readxl package:

download_data(
  domain = "Damodaran",
  dataset = "wacc",
  region = "us",
  year = 2023:2024
)
#> # A tibble: 192 × 14
#>    region  year industry  number_of_firms  beta cost_of_equity equity_to_capital
#>    <chr>  <int> <chr>               <int> <dbl>          <dbl>             <dbl>
#>  1 us      2023 Advertis…              57 1.37          0.102              0.748
#>  2 us      2023 Aerospac…              70 1.08          0.0883             0.797
#>  3 us      2023 Air Tran…              25 1.27          0.0971             0.381
#>  4 us      2023 Apparel                38 1.19          0.0938             0.672
#>  5 us      2023 Auto & T…              34 1.52          0.109              0.763
#>  6 us      2023 Auto Par…              39 1.34          0.101              0.722
#>  7 us      2023 Bank (Mo…              15 1.06          0.0876             0.316
#>  8 us      2023 Banks (R…             625 0.461         0.0600             0.495
#>  9 us      2023 Beverage…              19 1.13          0.0909             0.803
#> 10 us      2023 Beverage…              29 0.764         0.0740             0.854
#> # ℹ 182 more rows
#> # ℹ 7 more variables: sd_stock <dbl>, cost_of_debt <dbl>, tax_rate <dbl>,
#> #   after_tax_cost_of_debt <dbl>, debt_to_capital <dbl>, wacc <dbl>,
#> #   wacc_local <dbl>

To 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. CPIAUCNS

To download the FRED-MD / FRED-QD (McCracken-Ng) macroeconomic databases, a curated, balanced panel of monthly or quarterly macro series with McCracken-Ng stationarity transform codes. Set transform = TRUE to apply the transforms, and vintage to a "YYYY-MM" label or "all" to access historical (real-time) releases:

download_data(
  domain = "FRED",
  dataset = "FRED-MD",
  transform = TRUE
)
#> # A tibble: 801 × 127
#>    date             RPI   W875RX1 DPCERA3M086SBEA  CMRMTSPLx   RETAILx   INDPRO
#>    <date>         <dbl>     <dbl>           <dbl>      <dbl>     <dbl>    <dbl>
#>  1 1959-01-01 NA        NA               NA       NA         NA        NA      
#>  2 1959-02-01  0.00388   0.00362          0.0103   0.00734    0.00731   0.0194 
#>  3 1959-03-01  0.00646   0.00732          0.00940 -0.00337    0.00832   0.0143 
#>  4 1959-04-01  0.00651   0.00703         -0.00362  0.0199     0.000616  0.0211 
#>  5 1959-05-01  0.00580   0.00662          0.0120   0.00684    0.00780   0.0150 
#>  6 1959-06-01  0.00307   0.00301          0.00364 -0.0000968  0.00906   0.00114
#>  7 1959-07-01 -0.000580 -0.000762        -0.00339  0.0122    -0.000330 -0.0242 
#>  8 1959-08-01 -0.00565  -0.00575          0.00600 -0.0525     0.00636  -0.0345 
#>  9 1959-09-01  0.000763  0                0.0100   0.0147    -0.0132   -0.00121
#> 10 1959-10-01  0.00127   0.00117         -0.00683  0.00162    0.00729  -0.00729
#> # ℹ 791 more rows
#> # ℹ 120 more variables: IPFPNSS <dbl>, IPFINAL <dbl>, IPCONGD <dbl>,
#> #   IPDCONGD <dbl>, IPNCONGD <dbl>, IPBUSEQ <dbl>, IPMAT <dbl>, IPDMAT <dbl>,
#> #   IPNMAT <dbl>, IPMANSICS <dbl>, IPB51222S <dbl>, IPFUELS <dbl>,
#> #   CUMFNS <dbl>, HWI <dbl>, HWIURATIO <dbl>, CLF16OV <dbl>, CE16OV <dbl>,
#> #   UNRATE <dbl>, UEMPMEAN <dbl>, UEMPLT5 <dbl>, UEMP5TO14 <dbl>,
#> #   UEMP15OV <dbl>, UEMP15T26 <dbl>, UEMP27OV <dbl>, CLAIMSx <dbl>, …

To 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: 506 × 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           72.1
#>  4 AAPL   2020-01-07 108872000  75.0  74.4  75.2  74.6           71.8
#>  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           74.5
#>  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           75.2
#> 10 AAPL   2020-01-15 121923600  78.0  77.4  78.9  77.8           74.9
#> # ℹ 496 more rows

You 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>

Download WRDS Data

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.

A WRDS account alone is not enough to connect: you also need to enroll in two-factor authentication (Duo) and accept the WRDS terms of use on the WRDS website. When you connect, approve the Duo push notification on your device. See ?get_wrds_connection for the full setup checklist.

The WRDS downloads tell you when they connect and how long the download took. Pass quiet = TRUE to suppress these messages.

To download monthly CRSP data:

download_data(
  domain = "WRDS",
  dataset = "crsp_monthly",
  start_date = "2020-01-01",
  end_date = "2020-12-31"
)
#> ℹ Connecting to WRDS. Approve the Duo request if prompted.
#> ✔ Connected to WRDS. [4s]
#> 
#> ℹ Downloading CRSP monthly data from WRDS. This can take a few minutes.
#> ✔ Downloaded CRSP monthly data from WRDS. [3.5s]
#> 
#> # A tibble: 43,478 × 15
#>    permno date       calculation_date      ret   shrout    prc primaryexch siccd
#>     <int> <date>     <date>              <dbl>    <dbl>  <dbl> <chr>       <int>
#>  1  14313 2020-01-01 2020-01-31        0.00609   2.10e6   2.31 Q            3629
#>  2  14316 2020-01-01 2020-01-31       -0.158     6.54e7  16.2  Q            2834
#>  3  14317 2020-01-01 2020-01-31        0.129     2.54e7  79.7  Q            1542
#>  4  14318 2020-01-01 2020-01-31       -0.0380    6.84e7  31.9  Q            6351
#>  5  14325 2020-01-01 2020-01-31        0.276     5.94e7  76.0  Q            3841
#>  6  14328 2020-01-01 2020-01-31       -0.0994    5.21e7   6.52 N            7832
#>  7  14329 2020-01-01 2020-01-31        0.0171    2.49e8  44.1  N            5812
#>  8  14338 2020-01-01 2020-01-31       -0.0280    2.82e8 108.   N            7011
#>  9  14339 2020-01-01 2020-01-31        0.111     2.05e7   6.5  Q            6799
#> 10  14352 2020-01-01 2020-01-31        0.0295    3.92e7   8.73 Q            2834
#> # ℹ 43,468 more rows
#> # ℹ 7 more variables: listing_age <int>, mktcap <dbl>, mktcap_lag <dbl>,
#> #   prc_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"
)
#> ℹ Connecting to WRDS. Approve the Duo request if prompted.
#> ✔ Connected to WRDS. [4s]
#> 
#> ℹ Downloading Compustat annual data from WRDS. This can take a few minutes.
#> ✔ Downloaded Compustat annual data from WRDS. [1.3s]
#> 
#> # A tibble: 11,993 × 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 11,983 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"
)
#> ℹ Connecting to WRDS. Approve the Duo request if prompted.
#> ✔ Connected to WRDS. [4.1s]
#> 
#> ℹ Downloading CRSP-Compustat links from WRDS. This can take a few minutes.
#> ✔ Downloaded CRSP-Compustat links from WRDS. [1.1s]
#> 
#> # 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 2026-10-25
#>  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 rows

To 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"
)
#> ℹ Connecting to WRDS. Approve the Duo request if prompted.
#> ✔ Connected to WRDS. [3.6s]
#> 
#> ℹ Downloading enhanced TRACE data from WRDS. This can take a few minutes.
#> ✔ Downloaded enhanced TRACE data from WRDS. [1.2s]
#> 
#> # 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>

Factor Library Tools

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 = "assetgrowth"
)
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A data frame: 779 × 19
#>        id date            ret sorting_variable min_size_quantile min_stock_price
#>     <int> <date>        <dbl> <chr>                        <dbl>           <dbl>
#>  1 2.12e6 1960-02-01 -0.0272  assetgrowth                    0.2              NA
#>  2 2.12e6 1960-03-01 -0.0590  assetgrowth                    0.2              NA
#>  3 2.12e6 1960-04-01 -0.0156  assetgrowth                    0.2              NA
#>  4 2.12e6 1960-05-01 -0.0751  assetgrowth                    0.2              NA
#>  5 2.12e6 1960-06-01  0.00232 assetgrowth                    0.2              NA
#>  6 2.12e6 1960-07-01  0.0306  assetgrowth                    0.2              NA
#>  7 2.12e6 1960-08-01 -0.0193  assetgrowth                    0.2              NA
#>  8 2.12e6 1960-09-01  0.0360  assetgrowth                    0.2              NA
#>  9 2.12e6 1960-10-01  0.0302  assetgrowth                    0.2              NA
#> 10 2.12e6 1960-11-01 -0.0283  assetgrowth                    0.2              NA
#> # ℹ 769 more rows
#> # ℹ 13 more variables: min_listing_age <int>, exclude_financials <lgl>,
#> #   exclude_utilities <lgl>, exclude_negative_book_equity <lgl>,
#> #   exclude_negative_earnings <lgl>, sorting_variable_lag <chr>,
#> #   rebalancing <chr>, n_portfolios_main <dbl>, 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()

Other Helpers

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()