Get Started

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE
)

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 addressed, 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.

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:

You get an API key from our website EventStudyTools. In the first step we need to authenticate to the web API. There are three ways to handle this two parameters:

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

Option 1 and 2: 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)

# use EventStudy estAPIKey function
estAPIKey(apiKey)

# initialize object
estSetup <- EventStudyAPI$new()

Option 2: Set the API Key and URL 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 analyses 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 default parameters.

Event Study with Defaults Parameters

There will be soon a separate vignette for setting parameters.

Event Study Types

Our API offers different types of Event Studies:

Default parameters for all type of above Event Studies are:

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:

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 arbitrarily):

  1. A request file where the structure of the Event Study is defined
  2. A firm data file containing the stock data for each firm defined in the request file
  3. 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 describe 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:

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.

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)

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.

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 Data: 03_MarketData.csv

This file is similary structured as 02_FirmData.csv:

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))

You are also able to apply a Fama-French 3-Factor Model or a Fama-French Momentum-4-Factor Model. This will change the necessary 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

  1. Event Study: Parameters: In this Vignette we show how you can set parameters and which parameters are allowed.
  2. Data Preparation: In this Vignette we show you how to get and prepare data for your Event Study from R.
  3. 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

How to Cite

Please cite our work in your publication.



Try the EventStudy package in your browser

Any scripts or data that you put into this service are public.

EventStudy documentation built on March 31, 2023, 5:43 p.m.