| Title: | Data and Code for Financial Accounting Research |
| Version: | 0.3.2 |
| Description: | Handy functions and data to support a course book for accounting research. Gow, Ian D. and Tongqing Ding (2024) 'Empirical Research in Accounting: Tools and Methods' https://iangow.github.io/far_book/. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.3.2 |
| BugReports: | https://github.com/iangow/farr/issues |
| Imports: | dbplyr (≥ 2.2.0), dplyr, magrittr, rlang, tidyr, tibble, readr, stringr, DBI, lubridate, rpart |
| Depends: | R (≥ 3.5.0) |
| Suggests: | RPostgres, duckdb, knitr, rmarkdown, testthat (≥ 3.0.0), spelling |
| Config/testthat/edition: | 3 |
| Language: | en-US |
| URL: | https://github.com/iangow/farr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-01 06:58:45 UTC; igow |
| Author: | Ian Gow  [aut,
cre] |
| Maintainer: | Ian Gow <iandgow@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-01 07:10:02 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
AAER dates from SEC
Description
A data set containing dates and descriptions for AAERs
Usage
aaer_dates
Format
A tibble with 2,920 rows and 4 variables:
- aaer_num
AAER number
- aaer_date
Date
- aaer_desc
Description
- year
Year of AAER
AAERs from Bao et al. (2020)
Description
A data set containing AAER firms-years used in Bao et al. (2020).
Usage
aaer_firm_year
Format
A tibble with 415 rows and 4 variables:
- p_aaer
AAER identifier
- gkvey
GVKEY (firm identifier)
- min_year
First affected year
- max_year
Last affected year
Source
Dates for Apple Events
Description
A data set containing the dates of Apple media events since 2005.
Usage
apple_events
Format
A tibble with 47 rows and 3 variables:
- event
Description of event
- event_date
First date of event
- end_event_date
Last date of event
Source
https://en.wikipedia.org/wiki/List_of_Apple_Inc._media_events
Area under curve
Description
A function returning AUC.
Usage
auc(scores, response)
Arguments
scores |
Probability that response is true or 1. |
response |
Responses coded as logical or 0-or-1. |
Value
vector including AUC
Source
https://stackoverflow.com/questions/4903092/calculate-auc-in-r
Australian bank fundamental data
Description
A data set containing fundamental financial information for Australian banks.
Usage
aus_bank_funds
Format
A tibble with 283 rows and 7 variables:
- gvkey
GVKEY (firm identifier)
- datadate
Fiscal year-end
- at
Total assets
- ib
Income before extraordinary items
- xi
Extraordinary items
- do
Income from discontinued operations
Australian bank stock market data
Description
A data set containing fundamental financial information for Australian banks.
Usage
aus_bank_rets
Format
A tibble with 3,047 rows and 4 variables:
- gvkey
GVKEY (firm identifier)
- datadate
Last trading date of month
- ret
Stock return for month
- mkt_cap
Market capitalization on datadate
Australian banks
Description
A data set containing identifying information for 10 Australian banks.
Usage
aus_banks
Format
A tibble with 10 rows and 3 variables:
- gvkey
GVKEY (firm identifier)
- ticker
Stock exchange ticker
- co_name
Bank name
Firm-years in RDD analysis of Bloomfield (2021)
Description
Firm-years in RDD analysis of Bloomfield (2021).
Usage
bloomfield_2021
Format
A tibble with 1,855 rows and 2 variables:
- fyear
Fiscal year
- permco
CRSP firm identifier (PERMCO)
Source
Tags on StackOverflow
Description
A data set containing data on tagged questions on StackOverflow
Usage
by_tag_year
Format
A tibble with 40,518 rows and 4 variables:
- year
Year
- tag
Tag
- number
Number of questions with tag during year
- year_total
Total number of questions with tag during year
Camp attendance
Description
A simulated data set related to camp attendance.
Usage
camp_attendance
Format
A tibble with 1,000 rows and 2 variables:
- id
Student identifier
- camp
Indicator for student attendance at camp
Data for CMSW
Description
Data on whistleblowers and enforcement actions from Call et al. (2018)
Usage
cmsw_2018
Format
A tibble with 1,133 rows and 31 variables:
- recid
CMSW record identifier
- firmpenalty
The total firm civil and criminal monetary penalties assessed against the firm, its parent and subsidiaries consisting of disgorgement, prejudgment interest, civil fines, criminal restitution, and criminal fines in millions of dollars
- otherpenalty
The total firm civil and criminal monetary penalties assessed against the agent firms and/or respondents (e.g., the audit firm, bankers, suppliers) in connection with the financial misrepresentation of the target firm, in millions of dollars
- emppenalty
The total civil and criminal penalties assessed against all employees consisting of disgorgement, prejudgment interest, civil fines, criminal restitution, and criminal fines in millions of dollars
- empprisonmos
Total incarceration consisting of jail, prison, home detention, and halfway house in months imposed upon employee respondents named in the enforcement action
- selfdealflag
An indicator variable equal to one if the violation includes self-dealing such as embezzlement and theft by respondents and equal to zero otherwise
- blckownpct
The percentage of blockholder ownership, defined as owners with at least five percent of common shares outstanding from the last 10-K or DEF 14A prior to the first public announcement the firm may be (is) subject to a regulatory enforcement action
- initabret
The value-weighted market-adjusted return measured at the close of trading on the initial public announcement date that the firm may be (is) subject to a regulatory enforcement action
- wbflag
An indicator variable equal to one if a whistleblower is associated with the enforcement action and equal to zero otherwise
- tousesox
Post-SOX action flag
- lnvioperiod
The natural logarithm of the total time the violation occurred in months as indicated in the regulatory enforcement proceedings
- bribeflag
An indicator variable equal to one if the enforcement actions includes charges under the Foreign Corrupt Practices Act for bribery of a foreign official and zero otherwise
- mobflag
An indicator variable equal to one if violation or any of the respondents were associated with a known organized crime family and zero otherwise
- deter
An indicator variable equal to one if the violation includes an offense for either option backdating, insider trading, or an offense related to an offering, IPO, merger, or reverse merger and equal to zero otherwise
- lnempcleveln
The natural logarithm of the total number of C-level respondents (e.g. CEO, COO, CFO, CAO, CMO, and CIO) named in the enforcement action
- lnuscodecnt
The natural logarithm of the total number of unique code sections and rules violated (charges) associated with the enforcement action
- viofraudflag
n indicator variable equal to one if fraud under 15 USC §§ 77q, 78j(b), or rules promulgated thereunder are included among the charges in the enforcement action
- misledflag
An indicator variable equal to one if the violation included violations of 17 CFR 240.13b2-2 that prohibits materially false or misleading statement to an accountant in connection with the preparation of financial statements and zero otherwise
- audit8flag
An indicator variable equal to one if the misreporting firm used a Big N auditor, and equal to zero otherwise
- exectermflag
An indicator variable equal to one if the firm terminated an executive respondent as a result of the violations and equal to zero otherwise
- coopflag
An indicator variable equal to one if the firm received credit in the assessment of penalties for cooperation as stated in regulatory enforcement documents during the course of the investigation and equal to zero otherwise
- impedeflag
An indicator variable equal to one if regulators acknowledged they were deliberately misled and/or charges were included for lying to investigators and equal to zero otherwise
- pctinddir
The percentage of the firm's directors that are independent from the last 10-K or DEF 14A prior to the first public announcement the firm may be (is) subject to a regulatory enforcement action
- recidivist
An indicator variable equal to one if the firm was previously the subject of a securities regulatory enforcement action and equal to zero otherwise
- lnmktcap
The natural logarithm of the market value of equity measured in millions of dollars prior to the first public announcement that the firm may be (is) subject to a regulatory enforcement action
- mkt2bk
The sum of market value of equity plus total assets minus total debt divided by total assets with market value determined below and total assets and total debt measured at the last fiscal year end prior to the first public announcement the firm may be (is) subject to a regulatory enforcement action
- lev
Total debt divided by total assets measured at the last fiscal year end prior to the first public announcement the firm may be (is) subject to a regulatory enforcement action
- lndistance
The natural logarithm of the distance in miles from the location of the firm's headquarters to the offices of the regulator assigned to the geographic area of the firm's headquarter location (closer of the SEC Regional Office or DOJ U.S. District Attorney).
- ff12
Fama-French industry code (12-industry)
- wbsource
Whistleblower data source
- wbtype
Whistleblower type: tipster or nontipster
Source
Data on accruals and auditor choice
Description
A data set containing data about accruals for 2,000 firms.
Usage
comp
Format
A tibble with 16,237 rows and 14 variables:
- gvkey
GVKEY (firm identifier)
- datadate
Fiscal year-end
- fyear
Fiscal year
- big_n
Indicator for Big Four auditor
- ta
Total accruals (scaled by assets)
- roa
Return on assets
- cfo
Cash flow from operating activities (scaled by assets)
- size
Size
- lev
Leverage
- mtb
Market-to-book ratio
- inv_at
1/Total assets
- d_sale
Change in revenue
- d_ar
Change in accounts receivable
- ppe
Property, plant & equipment (scaled by assets)
Confusion statistics.
Description
A function returning sensitivity and precision.
Usage
confusion_stats(scores, response, predicted = NULL, k = NULL)
Arguments
scores |
Probability that response is true or 1. |
response |
Responses coded as logical or 0-or-1. |
predicted |
Predicted value coded as 0-or-1. |
k |
Percentage to classify as TRUE or 1. |
Value
vector including sensitivity and precision
Firm-years for replication of Fang, Huang and Karpoff (2016)
Description
A data set containing the GVKEYs and datadates for firm-years used in Fang, Huang and Karpoff (2016).
Usage
fhk_firm_years
Format
A tibble with 60,272 rows × 2 variables.
- gvkey
GVKEY (firm identifier)
- datadate
Fiscal year-end
Source
Treatment indicators for SHO pilot firms
Description
A data set containing the tickers, GVKEYs, and treatment indicator for SHO pilot program. i
Usage
fhk_pilot
Format
A tibble with 3,030 rows × 4 variables.
- ticker
Ticker
- gvkey
GVKEY (firm identifier)
- permno
PERMNO (CRSP security identifier)
- pilot
SHO pilot program treatment indicator
Source
Form deciles
Description
Calculate deciles for a variable.
Usage
form_deciles(x)
Arguments
x |
A vector for which deciles are to be calculated. |
Value
vector
Examples
library(farr)
library(dplyr, warn.conflicts = FALSE)
df <-
tibble(x = rnorm(100)) %>%
mutate(dec_x = form_deciles(x))
df
Produce a table mapping announcements to trading dates
Description
Produce a table mapping announcements to trading dates.
See vignette("wrds-conn", package = "farr") for more on using this function.
Usage
get_annc_dates(conn)
Arguments
conn |
connection to a PostgreSQL database |
Value
tbl_df
Examples
## Not run:
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
library(RPostgres)
pg <- dbConnect(Postgres())
get_annc_dates(pg)
## End(Not run)
## End(Not run)
Produce a table of cumulative event returns
Description
Produce a table of event returns from CRSP.
Usage
get_event_cum_rets(
data,
conn,
permno = "permno",
event_date = "event_date",
win_start = 0,
win_end = 0,
end_event_date = NULL,
suffix = ""
)
Arguments
data |
data frame containing data on events |
conn |
connection to a PostgreSQL database |
permno |
string representing column containing PERMNOs for events |
event_date |
string representing column containing dates for events |
win_start |
integer representing start of trading window (e.g., -1) |
win_end |
integer representing start of trading window (e.g., 1) |
end_event_date |
string representing column containing ending dates for events |
suffix |
Text to be appended after "ret" in variable names |
Value
tbl_df
Examples
## Not run:
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
library(RPostgres)
pg <- dbConnect(Postgres())
events <- tibble(permno = c(14593L, 10107L),
event_date = as.Date(c("2019-01-31", "2019-01-31")))
get_event_cum_rets(events, pg)
## End(Not run)
## End(Not run)
Produce a table of cumulative event returns using monthly data
Description
Produce a table of event returns from CRSP
See vignette("wrds-conn", package = "farr") for more on using this function.
Usage
get_event_cum_rets_mth(
data,
conn,
permno = "permno",
event_date = "event_date",
win_start = 0,
win_end = 0,
end_event_date = NULL,
suffix = ""
)
Arguments
data |
data frame containing data on events |
conn |
connection to a PostgreSQL database |
permno |
string representing column containing PERMNOs for events |
event_date |
string representing column containing dates for events |
win_start |
integer representing start of trading window (e.g., -1) in months |
win_end |
integer representing start of trading window (e.g., 1) in months |
end_event_date |
string representing column containing ending dates for events |
suffix |
Text to be appended after "ret" in variable names. |
Value
tbl_df
Examples
## Not run:
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
library(RPostgres)
pg <- dbConnect(Postgres())
events <- tibble(permno = c(14593L, 10107L),
event_date = as.Date(c("2019-01-31", "2019-01-31")))
get_event_cum_rets_mth(events, pg)
## End(Not run)
## End(Not run)
Produce a table mapping announcements to trading dates
Description
Produce a table of event dates for linking with CRSP.
See vignette("wrds-conn", package = "farr") for more on using this function.
Usage
get_event_dates(
data,
conn,
permno = "permno",
event_date = "event_date",
win_start = 0,
win_end = 0,
end_event_date = NULL
)
Arguments
data |
data frame containing data on events |
conn |
connection to a PostgreSQL database |
permno |
string representing column containing PERMNOs for events |
event_date |
string representing column containing dates for events |
win_start |
integer representing start of trading window (e.g., -1) |
win_end |
integer representing start of trading window (e.g., 1) |
end_event_date |
string representing column containing ending dates for events |
Value
tbl_df
Examples
## Not run:
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
pg <- dbConnect(RPostgres::Postgres())
events <- tibble(permno = c(14593L, 10107L),
event_date = as.Date(c("2019-01-31", "2019-01-31")))
get_event_dates(events, pg, win_start = -3, win_end = + 3)
## End(Not run)
## End(Not run)
Produce a table of event returns
Description
Produce a table of event returns from CRSP.
See vignette("wrds-conn", package = "farr") for more on using this function.
Usage
get_event_rets(
data,
conn,
permno = "permno",
event_date = "event_date",
win_start = 0,
win_end = 0,
end_event_date = NULL
)
Arguments
data |
data frame containing data on events |
conn |
connection to a PostgreSQL database |
permno |
string representing column containing PERMNOs for events |
event_date |
string representing column containing dates for events |
win_start |
integer representing start of trading window (e.g., -1) |
win_end |
integer representing start of trading window (e.g., 1) |
end_event_date |
string representing column containing ending dates for events |
Value
tbl_df
Examples
## Not run:
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
pg <- dbConnect(RPostgres::Postgres())
events <- tibble(permno = c(14593L, 10107L),
event_date = as.Date(c("2019-01-31", "2019-01-31")))
get_event_rets(events, pg, win_start = -3, win_end = +3) %>%
select(permno, event_date, date, ret)
## End(Not run)
## End(Not run)
Fetch Fama-French industry grouping.
Description
Fetch Fama-French industry grouping from Ken French's website.
Usage
get_ff_ind(ind)
Arguments
ind |
Fama-French industry grouping (e.g., 11, 48) |
Value
tbl_df
Examples
## Not run:
get_ff_ind(5)
## End(Not run)
Generate simulated data as described in Gow, Ormazabal and Taylor (2010).
Description
Function to generate simulated panel data as described in Gow, Ormazabal and Taylor (2010).
Usage
get_got_data(N = 400, T = 20, Xvol, Evol, rho_X, rho_E)
Arguments
N |
Number of firms |
T |
Number of years |
Xvol |
Cross-sectional correlation of X |
Evol |
Cross-sectional correlation of errors |
rho_X |
Autocorrelation coefficient for firm-effect portion of X |
rho_E |
Autocorrelation coefficient for firm-effect portion of epsilon |
Value
tibble
Source
https://www.jstor.org/stable/20744139
Examples
set.seed(2021)
test <- get_got_data(N = 500, T = 10, Xvol = 0.75,
Evol = 0.75, rho_X = 0.5, rho_E = 0.5)
Period for Inevitable Disclosure Doctrine (IDD)
Description
Periods defined by precedent-setting legal cases adopting or rejecting the Inevitable Disclosure Doctrine (IDD) by state.
Usage
get_idd_periods(min_date, max_date)
Arguments
min_date |
First date of sample period |
max_date |
Last date of sample period |
Details
Three kinds of period by state:
Pre-adoption
Post-adoption
Post-rejection
Value
tibble with four columns: state, period_type, start_date, end_date
Examples
idd_periods <- get_idd_periods(min_date = "1994-01-01",
max_date = "2010-12-31")
idd_periods
Create a table of with cut-offs for size portfolios
Description
Create a table of with cut-offs for size portfolios
Usage
get_me_breakpoints()
Value
tbl_df
Examples
library(dplyr, warn.conflicts = FALSE)
get_me_breakpoints() %>% filter(month == '2022-04-01')
Create a table of monthly returns for size portfolios
Description
Create a table of monthly returns for size portfolios
Usage
get_size_rets_monthly()
Value
tbl_df
A function returning data on test_scores.
Description
A function returning simulated data on test_scores.
Usage
get_test_scores(
effect_size = 15,
n_students = 1000L,
n_grades = 4L,
include_unobservables = FALSE,
random_assignment = FALSE
)
Arguments
effect_size |
Effect of attending camp on subsequent test scores |
n_students |
Number of students in simulated data set |
n_grades |
Number of grades in simulated data set |
include_unobservables |
Include talent in returned data (TRUE or FALSE) |
random_assignment |
Is assignment to treatment completely random? (TRUE or FALSE) |
Value
tbl_df
Examples
set.seed(2021)
library(dplyr, warn.conflicts = FALSE)
get_test_scores() %>% head()
Produce a table mapping dates on CRSP to "trading days"
Description
Produce a table mapping dates on CRSP to "trading days". Returned table has two columns: date, a trading date on CRSP; td, a sequence of integers ordered by date.
Usage
get_trading_dates(conn)
Arguments
conn |
connection to a PostgreSQL database |
Value
tbl_df
Examples
## Not run:
library(DBI)
library(dplyr, warn.conflicts = FALSE)
pg <- dbConnect(RPostgres::Postgres())
get_trading_dates(pg) %>%
filter(between(date, as.Date("2022-03-18"), as.Date("2022-03-31")))
## End(Not run)
GVKEY-CIK links
Description
Link table from GVKEYs to CIKs
Usage
gvkey_ciks
Format
A tibble with 78,339 rows and 5 variables:
- gvkey
GVKEY (Compustat firm identifier)
- iid
Issue ID
- cik
CIK (SEC firm identifier)
- first_date
First link date
- last_date
Last link date
Dates for Inevitable Disclosure Doctrine (IDD)
Description
Dates of precedent-setting legal cases adopting or reject the Inevitable Disclosure Doctrine (IDD) by state.
Usage
idd_dates
Format
A tibble with 24 rows and 3 variables:
- state
Two-letter state abbreviation
- idd_date
Date of precedent-setting legal case
- idd_type
Either "Adopt" or "Reject"
Source
doi:10.1016/j.jfineco.2018.02.008
Data on public float
Description
Data on public float of listed companies from Iliev (2010).
Usage
iliev_2010
Format
A tibble with 7,214 and 9 variables:
- gvkey
Compustat firm identifier (GVKEY)
- fyear
Fiscal year
- fdate
Date of end of fiscal year
- pfdate
Date for public float value
- pfyear
Year for public float value
- publicfloat
Public float in $ million
- mr
Indicator for filing of a management report
- af
Indicator for accelerator filer
- cik
SEC firm identifier (CIK)
Source
doi:10.1111/j.1540-6261.2010.01564.x
GVKEYs used in Li, Lin and Zhang (2018)
Description
GVKEYs used in Li, Lin and Zhang (2018)
Usage
llz_2018
Format
A tibble with 5,830 rows and 1 variable:
- gvkey
GVKEY
Source
Function to load parquet file into database.
Description
Function to read data from a parquet file data_dir/schema/table_name.parquet into a table in the DuckDB database at conn.
Usage
load_parquet(conn, table, schema = "", data_dir = Sys.getenv("DATA_DIR"))
Arguments
conn |
DuckDB connection |
table |
Name of table to be loaded |
schema |
Database schema for table |
data_dir |
Directory for data repository |
Value
Remote data frame in conn
Data on firms suffering natural disasters
Description
Data on firms suffering natural disasters based on the sample in Michels (2017).
Usage
michels_2017
Format
A tibble with 423 rows and 12 variables:
- cusip
CUSIP supplied by Michels (2017)
- eventdate
Date of relevant natural disaster supplied by Michels (2017)
- cik
Matched CIK (SEC firm identifier)
- permno
Matched PERMNO (CRSP security identifier)
- gvkey
Matched GVKEY (Compustat firm identifier)
- date_filed
Date of next filing of type 10-Q, 10-K, 10QSB, 10-K405 after event
- form_types
List of relevant form types filed on date_filed
- next_period_end
Next fiscal period-end after event date
- next_fqtr
Fiscal quarter of next period-end after event date
- prev_period_end
Last fiscal period-end before event date
- prev_fqtr
Fiscal quarter of last period-end before event date
- recognize
Indicator for event being recognized (next_period_end before date_filed)
Source
Calculate metric: NDCG at k
Description
A function returning NDCG-at-k metric.
Usage
ndcg(scores, response, k = 0.01)
Arguments
scores |
Probability that response is true or 1. |
response |
Responses coded as logical or 0-1. |
k |
Percentage to classify as TRUE or 1. |
Value
vector including sensitivity and precision
Save WRDS table as parquet file.
Description
Function to get data from a table on the WRDS PostgreSQL server and save to local parquet file using DuckDB.
Usage
pg_to_parquet(table_name, schema, data_dir = Sys.getenv("DATA_DIR"))
Arguments
table_name |
Name of table on WRDS |
schema |
Database schema for table |
data_dir |
Directory for data repository |
Value
Number of rows created
A function returning data for a ROC plot.
Description
A function returning data for a ROC plot.
Usage
roc(scores, response)
Arguments
scores |
Probability that response is true or 1. |
response |
Responses coded as logical or 0-or-1. |
Value
tbl_df
Random under-sampling function
Description
Function to create temporary training dataset using distribution implied by w.
Usage
rus(y_train, ir = 1)
Arguments
y_train |
df on the target variable. |
ir |
Imbalance ratio. Specifies how many times the under-sampled majority instances are over minority instances. |
Details
Following MATLAB, function samples observations of the minority class with replacement and observations of the majority class without replacement.
Value
vector
RUSBoost for two-class problems
Description
RUSBoost for two-class problems
Usage
rusboost(formula, df, size, ir = 1, learn_rate = 1, rus = TRUE, control)
Arguments
formula |
A formula specify predictors and target variable. Target variable should be a factor of 0 and 1. Predictors can be either numerical and categorical. |
df |
A df frame used for training the model, i.e. training set. |
size |
Ensemble size, i.e. number of weak learners in the ensemble model |
ir |
Imbalance ratio. Specifies how many times the under-sampled majority instances are over minority instances. |
learn_rate |
Default of 1. |
rus |
TRUE for random undersampling; FALSE for AdaBoost with full sample |
control |
Control object passed onto rpart function. |
Value
rusboost object
Russell 3000 stocks at time of SEC Reg SHO sample formation.
Description
A data set containing the tickers and company names for Russell 3000 at time SEC created the pilot sample. Data are created from sample supplied by FHK.
Usage
sho_r3000
Format
A tibble with 3000 rows × 2 variables.
- russell_ticker
Ticker
- russell_name
Company name
Source
Russell 3000 sample used by SEC with GVKEYs
Description
A data set containing the tickers, PERMNOs, GVKEYs, and treatment assignments for Russell 3000 sample used by SEC.
Usage
sho_r3000_gvkeys
Format
A tibble with 2,951 rows × 3 variables.
- ticker
Ticker
- permno
PERMNO (CRSP security identifier)
- gvkey
GVKEY (Compustat firm identifier)
- pilot
Indicator for stock being part of Reg SHO pilot program
Source
https://iangow.github.io/far_book/natural-revisited.html#the-sho-pilot-sample
Russell 3000 sample used by SEC
Description
A data set containing the tickers, PERMNOs, and treatment assignments for Russell 3000 sample used by SEC.
Usage
sho_r3000_sample
Format
A tibble with 2,954 rows × 3 variables.
- ticker
Ticker
- permno
PERMNO (CRSP security identifier)
- pilot
Indicator for stock being part of Reg SHO pilot program
Source
https://iangow.github.io/far_book/natural-revisited.html#the-sho-pilot-sample
Tickers of pilot firms for Reg SHO.
Description
A data set containing the tickers and company names for pilot firms from Reg SHO pilot. Data are scraped from the SEC's own website.
Usage
sho_tickers
Format
A tibble with 986 rows × 2 variables.
- ticker
Ticker
- co_name
Company name
Source
https://www.sec.gov/rule-release/34-50104
Data on firm headquarters based on SEC EDGAR filings
Description
Data on firm headquarters based on SEC EDGAR filings. Dates related to SEC filing dates. Rather than provide dates for all filings, data are aggregated into groups of filings by state and CIK and dates are collapsed into windows over which all filings for a given CIK were associated with a given state. For example, CIK 0000037755 has filings with a CA headquarters from 1994-06-02 until 1996-03-25, then filings with an OH headquarters from 1996-05-30 until 1999-04-05, then filings with a CA headquarters from 1999-06-11 onwards. To ensure continuous coverage over the sample period, it is assumed that any change in state occurs the day after the last observed filing for the previous state.
Usage
state_hq
Format
A tibble with 53,133 rows and 4 variables:
- cik
SEC's Central Index Key (CIK)
- ba_state
Two-letter abbreviation of state
- min_date
Date of first filing with CIK-state combination in a contiguous series of filings
- max_date
Date of last filing with CIK-state combination in a contiguous series of filings
Source
https://sraf.nd.edu/sec-edgar-data/10-x-header-data/
Version of system.time() that works with assignment
Description
Print CPU (and other) times that expr used, return value of expr
Usage
system_time(expr)
Arguments
expr |
Valid R expression to be timed, evaluated and returned |
Value
Result of evaluating expr
Test scores
Description
A simulated data set of test scores.
Usage
test_scores
Format
A tibble with 4,000 rows and 3 variables:
- id
Student identifier
- grade
School grade at time of test
- score
Test score
Truncate a vector.
Description
Truncate a vector at prob and 1 - prob. Extreme values are turned in NA values.
Usage
truncate(x, prob = 0.01, p_low = prob, p_high = 1 - prob)
Arguments
x |
A vector to be winsorized |
prob |
Level (two-sided) for winsorization (e.g., 0.01 gives 1% and 99%) |
p_low |
Optional lower level for winsorization (e.g., 0.01 gives 1%) |
p_high |
Optional upper level for winsorization (e.g., 0.99 gives 99%) |
Value
vector
Examples
trunced <- truncate(1:100, prob = 0.05)
min(trunced, na.rm = TRUE)
max(trunced, na.rm = TRUE)
Customer names that represent non-disclosures.
Description
Data to be combined with data in compsegd.seg_customer to create an indicator for non-disclosure of customer names.
Usage
undisclosed_names
Format
A tibble with 460 rows and 2 variables:
- cnms
Matches field in compsegd.seg_customer (WRDS)
- disclosure
Indicator that name is not disclosed
Winsorize a vector.
Description
Winsorize a vector at prob and 1 - prob.
Usage
winsorize(x, prob = 0.01, p_low = prob, p_high = 1 - prob)
Arguments
x |
A vector to be winsorized |
prob |
Level (two-sided) for winsorization (e.g., 0.01 gives 1% and 99%) |
p_low |
Optional lower level for winsorization (e.g., 0.01 gives 1%) |
p_high |
Optional upper level for winsorization (e.g., 0.99 gives 99%) |
Value
vector
Examples
winsorized <- winsorize(1:100, prob = 0.05)
min(winsorized, na.rm = TRUE)
max(winsorized, na.rm = TRUE)
Event dates from Zhang (2007)
Description
A data set containing the event dates used in Zhang (2007). Data obtained from Panel of Table of Zhang (2007). If an event spans multiple dates, then a row is included for each date.
Usage
zhang_2007_events
Format
A tibble with 30 rows × 3 variables.
- event
Identifier for the event
- date
Date of event
- event_desc
Description of the event
Source
doi:10.1016/j.jacceco.2007.02.002
Event windows from Zhang (2007)
Description
A data set containing the event windows used in Zhang (2007). Data obtained from Panel of Table of Zhang (2007).
Usage
zhang_2007_windows
Format
A tibble with 17 rows × 3 variables.
- event
Identifier for the event
- beg_date
First date of event window
- end_date
Last date of event window