Introducing EventStudy: A Powerful Tool for Event Study Analysis in R

Introduction

Event Study is a statistical method used to assess the impact of an event on the value of a firm. This method has been widely adopted in empirical finance and has been used to investigate the effects of corporate events like mergers, earnings announcements, and macroeconomic news on the value of firms.

While Event Study Analysis is a powerful tool, conducting these analyses in R can often be complicated and time-consuming. Until now. I’m excited to introduce EventStudy, a comprehensive and flexible R package designed to streamline the process of conducting Event Study Analyses.

Why Event Study?

Event Study is designed to offer a modular approach to conducting event studies in R. It allows you to apply common models, perform diagnostic tests, and extract results for further analysis. The package is designed to work seamlessly with your existing R workflow and offers a range of features that make conducting event studies in R a breeze.

Key Features

Features

The EventStudy package includes several features that make it a versatile tool for performing event study analyses:

Flexible Models and Diagnostic Tests: You can apply common models and perform diagnostic tests on them. The package is designed to be modular and adaptable, which means you can easily extend it with your own models and tests.
Custom Models: With EventStudy, you have the ability to apply your own market model. This means you can include external factors in your model that are specific to your study or industry.
Custom Test Statistics: You can apply your own test statistics for abnormal returns (AR), average abnormal returns (AAR), cumulative abnormal returns (CAR), and cumulative average abnormal returns (CAAR). This gives you full control over how you want to measure the impact of the event.
Result Extraction: You can extract the results of the event study for further analysis. For example, you might want to perform Cross-Sectional regression analysis. The package provides convenient functions for extracting confidence bands at each level and for each CAR and CAAR window.
Parallel Execution: If you are dealing with a large number of events, the package supports parallel execution. This allows you to take full advantage of your computer’s processing power to speed up the calculations.

Example: Dieselgate

To demonstrate the functionality and ease of use of EventStudy, let’s look at an example: an event study analysis of the Dieselgate scandal. This example demonstrates how to use the EventStudy package by conducting an event study analysis on the “Dieselgate” scandal. The scandal, which erupted in 2015, involved Volkswagen’s admission that it had installed software on its diesel cars to cheat on emissions tests. This significant event had substantial effects on the stock prices of Volkswagen and other automotive companies.

The following code will guide you through the steps of performing an event study analysis, from the initial setup to the extraction of results.

Initialization

The first step is to load necessary packages and data. This includes market data for the companies of interest and the index during the event study period.

#' warnings: false
library(tidyquant)
library(dplyr)
library(purrr)
library(readr)
library(DT)

library(EventStudy)

index_symbol = c("^GDAXI")
firm_symbols = c("VOW.DE", "PAH3.DE", "BMW.DE", "MBG.DE")

group <- c(rep("VW Group", 2), rep("Other", 2))
request_tbl <- cbind(c(1:4), firm_symbols, rep(index_symbol, 4), 
                     rep("18.09.2015", 4), 
                     group, rep(-10, 4), rep(10, 4), rep(-11, 4), rep(250, 4)) %>% 
  as_tibble()

names(request_tbl) <- c("event_id", "firm_symbol", "index_symbol", "event_date", 
                        "group", "event_window_start", "event_window_end", 
                        "shift_estimation_window", "estimation_window_length")

firm_symbols %>%
  tidyquant::tq_get(from = "2014-06-01", to = "2015-11-01") %>%
  dplyr::mutate(date = format(date, "%d.%m.%Y")) %>%
  dplyr::select(symbol, date, adjusted) -> firm_tbl

index_symbol %>%
  tidyquant::tq_get(from = "2014-06-01", to = "2015-11-01") %>%
  dplyr::mutate(date = format(date, "%d.%m.%Y")) %>%
  dplyr::select(symbol, date, adjusted) -> index_tbl

Both, the firm data as the index data should have the following structure:

symbol: Contains the symbol of the stock.
date: The date of the price information.
adjusted: The price of the stock at given date. The price column can be parametrized according to your needs when the task is defined. The default is adjusted.

DT::datatable(firm_tbl)

Define the Event Study

Next, we define the event study. This involves specifying the return calculation method, the market model, and the test statistics to be used. In this example, we use logarithmic returns, the market model, and the default AR and CAR T-Tests. Descriptions of the available models can be found on our website: Expected Return Models.

# Parametrization of the Event Study
log_return = LogReturn$new()
market_model = MarketModel$new()

For single events the standard AR and CAR T test are applied. For performing an Event Study on multiple events, currently the Cross-Sectional T Test (AAR and CAAR) is available. More a coming soon.

# Define single event test statistics
# Per default AR and CAR T-Tests are applied
single_event_tests = SingleEventStatisticsSet$new()

# Per default CSEct T Test is applied (AAR & CAAR)
multiple_event_tests = MultiEventStatisticsSet$new()

Your Event Study is then defined in a parameter set:

# Setup parameter set
param_set = ParameterSet$new(return_calculation      = log_return, 
                             return_model            = market_model,
                             single_event_statistics = single_event_tests,
                             multi_event_statistics  = multiple_event_tests)

Execute the Event Study

Now, with everything set up, we can execute the event study. This involves preparing the event study, fitting the model, and calculating the statistics. In the first step the task is defined. The firm and the index data gathered before as also the request data is necessary for performing the study.

est_task = EventStudyTask$new(firm_tbl, index_tbl, request_tbl)

Internally, firm and index data symbol and price column names are renamed. Afterwards a join is applied and the data is collected in a data frame with one event per row. Let’s have a look at the internal data frame:

DT::datatable(est_task$data_tbl)

DT::datatable(est_task$data_tbl$data[[1]])

DT::datatable(est_task$data_tbl$request[[1]])

The internal data structure is important for you if you plan to develop your own statistical or econometric model or test statistic.

est_task = prepare_event_study(est_task, param_set)

est_task$data_tbl$data[[1]]

est_task = fit_model(est_task, param_set)

est_task$data_tbl

est_task$data_tbl$model[[1]]

est_task = calculate_statistics(est_task, param_set)

est_task$data_tbl

est_task$data_tbl$ART[[1]]

est_task$data_tbl$CART[[1]]

est_task$aar_caar_tbl

est_task$aar_caar_tbl$CSectT[[1]]

Roadmap

While EventStudy already offers a powerful tool set for conducting event study analysis in R, development is actively ongoing. The roadmap for future features includes adding more test statistics, supporting long-term event study, volatility and volume event study with test statistics, and intraday event study. Stay tuned for more updates!

Conclusion

Event Study offers a streamlined and intuitive interface for conducting event study analysis in R, making it a valuable addition to the toolkit of any researcher or analyst in finance. Whether you’re assessing the impact of corporate events on stock prices or investigating the effects of macroeconomic news, Event Study provides a flexible and efficient solution.

Simon Mueller

2026-09-06