Motivation

You can manually adjust all parameters in your R environment. Save this object and use it in all your Event Studies. This vignette gives you an overview on how to set the correct parameters in R.

Options

You are able to set some general options in the R option object. This enables you to setup your API credentials for all R sessions.

EventStudy.URL: The API url (default: see bottom)
EventStudy.KEY: The API key (default: NULL)
EventStudy.verbose: Verbose level (range: 1 - 3, default: 3)
EventStudy.tryAttempts: Attempts to retry http request (default: 3)

The options can be changed by the options function in R, e.g.

options(EventStudy.URL = "http://api.eventstudytools.com")

The options can be retained by the

getOption("EventStudy.tryAttempts")

Performing an Event Study (ARC, AVC, or AVyC)

After you have set your paramaeters, you can easily perform your Event Study:

apiUrl <- "http://api.eventstudytools.com"
apiKey <- "Please insert your key here"

# Setup API Connection
estSetup <- EventStudyAPI$new(apiUrl)
estSetup$authentication(apiKey)

# Perform Event Study
estResults <- estSetup$performEventStudy(estParams     = volatilityEstParams, 
                                         dataFiles     = c("request_file" = "01_RequestFile.csv",
                                                           "firm_data"    = "02_firmData.csv",
                                                           "market_data"  = "03_marketData.csv"),
                                         downloadFiles = T, # download result files
                                         checkFiles    = T) # check input files

When you set your url or api key by EventStudy.URL and EventStudy.KEY then just leave input parameters in .\(new()* and *.\)authentication() empty. A default key and url can be set by:

options(EventStudy.KEY = apiKey)

General Event Study Parameters

There are several parameters that are valid for all type of Event Studies (Return, Volatility, and Volume Event Study). Each type of Event Study has its own R6 object that has to be initialized before you want to perform an Event Study.

ARCApplicationInput: Return Event Study
AVCApplicationInput: Volume Event Study
AVyCApplicationInput: Volatility Event Study

For the rest of this paragraph, we are working with a Return Event Study object. All parameters in this section can also applied to the other two objects.

returnEstParams <- ARCApplicationInput$new()

Return File Type

The return type defines the type of the result file. Possible options are

csv (default): comma-separated file with ; as delimiter,
xls: Excel 2007,
xlsx: Excel 2010 and newer, or
ods: OpenOffice Spreadsheet.

Usage:

returnEstParams$setResultFileType("xlsx")

Return Type

log (default): Return calculation by using log
simple: Simple return calculation

Usage:

returnEstParams$setReturnType("simple")

Adjustment of Non-trading Days

earlier: Take earlier trading day (Pick an earlier date if the event day falls on a non-trading day (e.g., a weekend day))
later (default): Take later trading day (Pick the next trading day if the event day falls on a non-trading day (e.g., a weekend day)).
keep: Keep non-trading day (Keep announcement dates even if they fall on a non-trading day (e.g., a weekend day))
skip: Skip respective observations (Skip observations with event dates falling on non-trading days (e.g., weekends))

Usage:

returnEstParams$setNonTradingDays("earlier")

Abnormal Return Event Study

For performing Return Event Studies you need to initialize an ARCApplicationInput R6 object.

returnEstParams <- ARCApplicationInput$new()

Benchmark Models

mm (default): Market Model
mm-sw: Scholes/Williams Model
cpmam: Comparison Period Mean Adjusted
ff3fm: Fama-French 3 Factor Model
ffm4fm: Fama-French-Momentum 4 Factor Model
garch: GARCH (1, 1) Model
egarch: EGARCH (1, 1) Model

Usage:

returnEstParams$setBenchmarkModel("mm-sw")

Test Statistics

Per default all test statistics are activated. A detailed description can be found on our website.

Parametric Test Statistics:

t-Test:
- art
- cart
- abhart
Cross-Sectional Test (CSect T):
- aart
- caart
- abhart
Time-Series Standard Deviation or Crude Dependence Test (CDA T):
- aarcdat
- caarcdat
Patell or Standardized Residual Test (Patell Z):
- aarptlz
- caarptlz
Kolari and Pynnönen adjusted Patell or Standardized Residual Test (Adjusted Patell Z):
- aaraptlz
- caaraptlz
Standardized Cross-Sectional or BMP Test (StdCSect Z):
- aarbmpz
- caarbmpz
Kolari and Pynnönen Adjusted Standardized Cross-Sectional or BMP Test (Adjusted StdCSect Z)
- aarabmpz
- caarabmpz
Skewness Corrected Test (Skewness Corrected T):
- aarskewadjt
- caarskewadjt
- abharskewadjt
Jackknife Test (Abbr.: Jackknife T)
- aarjackknivet
- caarjackknivet

Non-Parametric Test Statistics:

Corrado Rank Test (Rank Z):
- aarrankz
- caarrankz
Generalized Rank T Test (Generalized Rank T):
- aargrankt
- caargrankt
Generalized Rank Z Test (Generalized Rank Z):
- aargrankz
- caargrankz
Cowan Generalized Sign Test (Generalized Sign Z)
- aargsignz
- caargsignz

Usage:

returnEstParams$setTestStatistics(c("aarptlz", "aarrankz"))

Abnormal Volume Event Study

For performing Return Event Studies you need to initialize an AVCApplicationInput R6 object.

volumeEstParams <- AVCApplicationInput$new()

Benchmark Models

mm (default): Market Model
mm-sw: Scholes/Williams Model
cpmam: Comparison Period Mean Adjusted
ff3fm: Fama-French 3 Factor Model
ffm4fm: Fama-French-Momentum 4 Factor Model

Usage:

volumeEstParams$setBenchmarkModel("mm-sw")

Test Statistics

Per default all test statistics are activated. A detailed description can be found on our website.

Parametric Test Statistics:

t-Test:
- art
- cart
- abhart
Cross-Sectional Test (CSect T):
- aart
- caart
- abhart
Time-Series Standard Deviation or Crude Dependence Test (CDA T):
- aarcdat
- caarcdat
Patell or Standardized Residual Test (Patell Z):
- aarptlz
- caarptlz
Kolari and Pynnönen adjusted Patell or Standardized Residual Test (Adjusted Patell Z):
- aaraptlz
- caaraptlz
Standardized Cross-Sectional or BMP Test (StdCSect Z):
- aarbmpz
- caarbmpz
Kolari and Pynnönen Adjusted Standardized Cross-Sectional or BMP Test (Adjusted StdCSect Z)
- aarabmpz
- caarabmpz
Skewness Corrected Test (Skewness Corrected T):
- aarskewadjt
- caarskewadjt
- abharskewadjt
Jackknife Test (Abbr.: Jackknife T)
- aarjackknivet
- caarjackknivet

Non-Parametric Test Statistics:

Corrado Rank Test (Rank Z):
- aarrankz
- caarrankz
Generalized Rank T Test (Generalized Rank T):
- aargrankt
- caargrankt
Generalized Rank Z Test (Generalized Rank Z):
- aargrankz
- caargrankz
Cowan Generalized Sign Test (Generalized Sign Z)
- aargsignz
- caargsignz

Usage:

volumeEstParams$setTestStatistics(c("aarptlz", "aarrankz"))

Abnormal Volatility Event Study

For performing Return Event Studies you need to initialize an AVyCApplicationInput R6 object.

volatilityEstParams <- AVyCApplicationInput$new()

Benchmark Models

garch (default): Garch(1, 1) Model

Test Statistics

Per default all test statistics are activated. A detailed description can be found on our website.

Volatility Test Statistics:

Cross-Sectional Test for Abnormal Volatility
- aarcsvyt
Corrected Cross-Sectional Test for Abnormal Volatility
- aarcscvyt
AAR Volatility Ratio-Test
- aarlambdaz

Abnormal Return Test Statistics:

Cross-Sectional Test for Abnormal Return
- aarcsart
Cross-Sectional-Corrected Test for Abnormal Return
- aarcscart

Usage:

volatilityEstParams$setTestStatistics(c("aarptlz", "aarrankz"))

How to Cite

Please cite our work in your publication.

Event Study: Parameters

Dr. Simon Müller

2019-03-05

Motivation

Options

Performing an Event Study (ARC, AVC, or AVyC)

General Event Study Parameters

Return File Type

Return Type

Adjustment of Non-trading Days

Abnormal Return Event Study

Benchmark Models

Test Statistics

Abnormal Volume Event Study

Benchmark Models

Test Statistics

Abnormal Volatility Event Study

Benchmark Models

Test Statistics

How to Cite