Motivation
The attached sample data and request files were constructed to illustrate the use of the EventStudyTools R-package.
The files hold data about the addition of several well known firms to the S&P 500 index in the late 1990s. With this data, the R package will investigate for you the question whether a company’s stock value increases if the firm is added to the S&P 500 index. This is a common research question and has been adressed, among others, by Anthony W. Lynch and Richard R. Mendenha in a 1997 study: They found a positive effect of about 3.8% over the period starting the day after the announcement and ending the day before the effective date of the change (see: Mergers & Acquistions).
You can use our R-package to easily investigate such and similar questions.
Perform an Event Study from R
Authentication
For performing an Event Study with our API you need:
- API url (defaults to
http://api.eventstudytools.com
) - API key
You get a free API key from our website EventStudyTools. In the first step we need to authenticate to the web API. There are two ways to handle this two parameters:
apiUrl <- "http://api.eventstudytools.com"
apiKey <- "Please insert your key here"
Option 1: You can save API key and url in the options
object
# The URL is already set by default
# options(EventStudy.URL = apiUrl)
options(EventStudy.KEY = apiKey)
# initialize object
estSetup <- EventStudyAPI$new()
Option 2: or set them directly during the EventStudyAPI
R6-class
initialization
# Setup API Connection
estSetup <- EventStudyAPI$new(apiUrl)
estSetup$authentication(apiKey)
This API package is designed to perform all analysises we provide on our website. Furthermore, all parameters can be set. You are able to set every parameter in R (we will provide more details later), or you can perform a fast Event Study with the defaults.
Event Study with Defaults Parameters
There will be soon a separate vignette for setting parameters.
Type of Event Study
Our API offers different types of Event Studies:
- Return Event Study:
arc
- Trading Volume Event Study:
avc
- Abnormal Volatility Event Study:
avyc
Default parameters for all type of above Event Studies are:
- Benchmark model: Market Model for
arc
andavc
and GARCH(1, 1) Model foravyc
- Statistics: All available test statistics are calculated
- Result file type:
csv
- Return type calculation:
log
- Handling non-trading days:
later
The type of Event Study can be set by parameter:
estType <- "arc"
Data Files
By default all data files must be named as follows. Furthermore, they have to be in the current directory:
01_RequestFile.csv
02_firmData.csv
03_MarketData.csv
You are also able to set custom file names and paths by defining it in a named vector:
dataFiles <- c("request_file" = "01_RequestFile.csv",
"firm_data" = "02_firmData.csv",
"market_data" = "03_MarketData.csv")
Results
All results will be written by default into the directory ./results
. You can easily change this path by setting it as a parameter:
resultPath <- "results"
Attention
If the resultPath
do not exist, the R package will create this directory.
Performing the Event Study
Finally, the Event Study is performed by:
estResult <- estSetup$performDefaultEventStudy(estType = estType,
dataFiles = dataFiles,
destDir = resultPath)
It will write all result files into the result directory. Furthermore, results will be parsed into a R object.
Data File Description
For performing an Event Study we need three files (file names can be chosen arbitrarly):
- A request file where the structure of the Event Study is defined
- A firm data file containing the stock data for each firm defined in the request file
- A market data file containing the reference market data (multiple reference markets per study are possible)
All files must be saved without header, semi-colon separated and dates has to be in following format: 30.04.1997
. In next section we will descripe the file structure based on the S&P 500 example Event Study more detailed. You always find more information (if necessary) on our website: EventStudyTools.
We added the S&P 500 example Event Study to this package. The three necessary files can be easily generated by following command:
library(EventStudy)
getSP500ExampleFiles()
We named the request and data files in following manner:
01_RequestFile.csv
02_FirmData.csv
03_MarketData.csv
In your analysis, you can name them as you want.
Event Definitions: 01_RequestFile.csv
This csv file contains the event definitions. It contains 9 columns. The order must be in the following way, as the columns are not named in the csv.
- Event ID [Integer]: A unique event identifier.
- Firm ID [String]: The firm name or stock ID. This ID must match the ID in the firm data.
- Market ID [String]: The reference market ID. This ID must match the ID in the market data.
- Event Date [
30.04.1997
]: Date of the event.
- Grouping Variable [String]: This column is used to group events. Most test statistics are based on looking at groups.
- Start Event Window [Integer]: This integer value defines the start of the event window. This value must smaller or equal zero (e.g. trading days before event).
- End Event Window [Integer]: This integer value defines the end of the event window. This value must greater or equal zero (e.g. trading days after event).
- End of Estimation Window [Integer]: This negative value defines the end of the estimation window (counted from event day).
- Estimation Window Length [Integer]: Length of the estimation window.
In the following example, we have an event window of [-2, 2]
(an event window of length 5
), an estimation window of length 120
, and the estimation window ends 11
days before the event.
library(readr)
df <- readr::read_delim("01_RequestFile.csv", col_names = F, delim = ";")
names(df) <- c("Event ID", "Firm ID", "Market ID", "Event Date", "Grouping Variable", "Start Event Window", "End Event Window", "End of Estimation Window", "Estimation Window Length")
knitr::kable(head(df), pad=0)
Event ID | Firm ID | Market ID | Event Date | Grouping Variable | Start Event Window | End Event Window | End of Estimation Window | Estimation Window Length |
---|---|---|---|---|---|---|---|---|
75510 | Adobe Systems | SP500 | 30.04.1997 | Addition | -2 | 2 | -11 | 120 |
64390 | Progressive | SP500 | 25.07.1997 | Addition | -2 | 2 | -11 | 120 |
23473 | Cincinnati | SP500 | 16.12.1997 | Addition | -2 | 2 | -11 | 120 |
70500 | Coca-Cola Enterprises | SP500 | 01.10.1998 | Addition | -2 | 2 | -11 | 120 |
70519 | Travelers Group | SP500 | 01.10.1998 | Addition | -2 | 2 | -11 | 120 |
76149 | Safeway | SP500 | 05.11.1998 | Addition | -2 | 2 | -11 | 120 |
Attention
The first column (Event IDs) must be unique and numeric.
Firm Data: 02_FirmData.csv
The stock data for each firm defined in the request file. It contains 3 columns.
- Firm ID [String]: The firm or stock ID. This ID must match the ID in the Firm ID in the request file.
- Date [
30.04.1997
]: Dateof the closing price. - Closing Price [Double]: Closing price of volume of that day.
The following table shows the first 20
entries of our example firm data.
library(readr)
df <- readr::read_delim("02_FirmData.csv", col_names = F, delim = ";")
names(df) <- c("Firm ID", "Date", "Closing Price")
knitr::kable(head(df))
Firm ID | Date | Closing Price |
---|---|---|
Cincinnati | 01.10.1996 | 58.000 |
Cincinnati | 02.10.1996 | 57.000 |
Cincinnati | 03.10.1996 | 56.750 |
Cincinnati | 04.10.1996 | 57.500 |
Cincinnati | 07.10.1996 | 57.625 |
Cincinnati | 08.10.1996 | 57.750 |
Firm Data: 03_MarketData.csv
This file is similary structured as 02_FirmData.csv
:
- Market ID [String]: The market ID or stock ID. This ID must match the ID in the Market ID in the request file.
- Date [
30.04.1997
]: Date of the closing price. - Closing Price [Double]: Closing price of volume of that day.
The following table shows the first 20
entries of our example firm data.
library(readr)
df <- readr::read_delim("03_MarketData.csv", col_names = F, delim = ";")
names(df) <- c("Market ID", "Date", "Closing Price")
knitr::kable(head(df))
Market ID | Date | Closing Price |
---|---|---|
SP500 | 30.09.1996 | 100.0000 |
SP500 | 01.10.1996 | 100.2575 |
SP500 | 02.10.1996 | 100.9747 |
SP500 | 03.10.1996 | 100.7958 |
SP500 | 04.10.1996 | 102.0587 |
SP500 | 07.10.1996 | 102.3380 |
You are also able to apply a Fama-French 3-Factor Model or a Fama-French Momentum-4-Factor Model. This will change the neccessary data you need for performing an Event Study (e.g. by adding Fama-French Factors). You find more information at https://www.eventstudytools.com/instructions-axc.
More Vignettes
- Event Study: Parameters: In this Vignette we show how you can set parameters and which parameters are allowed.
- Graphics: In this Vignette we show you how to generate nice (static or interactive) graphs (coming soon)
- Data Preparation: In this Vignette we show you how to get and prepare data for your Event Study from R (coming soon)
- RStudio Addins: We wrote a RStudio addin for performing different types of Event Studies
- Abnormal Return Event Study
- Abnormal Volume Event Study
- Abnormal Volatility Event Study