Getting Started with EventStudy

Simon Mueller

2026-10-09

Introduction

An event study measures the impact of an event – a merger, earnings announcement, or macroeconomic shock – on the value of a firm. EventStudy offers a modular pipeline for conducting these analyses in R: apply common return models, run diagnostic and test statistics, and extract publication-ready results, all in a workflow that composes cleanly with the tidyverse.

This vignette walks through the core flow: define a task, run the study, inspect the results, and extract or plot them. It uses the Dieselgate scandal as a running example.

Quick Start

If you already have firm data, index data, and a request table (all defined below), the entire pipeline runs in a single call:

# Preview of the full pipeline — data objects are loaded and unpacked step-by-step below.
library(EventStudy)

# One-call shortcut: prepare -> fit -> calculate, in one step
est_task <- run_event_study(
  EventStudyTask$new(firm_tbl, index_tbl, request_tbl),
  ParameterSet$new(
    return_calculation      = LogReturn$new(),
    return_model            = MarketModel$new(),
    single_event_statistics = SingleEventStatisticsSet$new(),
    multi_event_statistics  = MultiEventStatisticsSet$new()
  )
)

est_task$aar_caar_tbl

The rest of this vignette unpacks the same pipeline step by step, so you can see – and customize – each stage.

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.

library(EventStudy)

# Load the bundled frozen Dieselgate dataset (replaces a live network download via
# tidyquant so this vignette builds fully offline and reproducibly on CRAN/pkgdown).
data(dieselgate)

firm_tbl    <- dieselgate$firm     # symbol / date / adjusted (4 firms, DD-MM-YYYY dates)
index_tbl   <- dieselgate$index    # symbol / date / adjusted (^GDAXI)
request_tbl <- dieselgate$request  # event_id / firm_symbol / index_symbol / event_date /
                                   # group / event_window_start / event_window_end /
                                   # shift_estimation_window / estimation_window_length

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

  1. symbol: Contains the symbol of the stock.
  2. date: The date of the price information.
  3. 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.
es_tt(head(firm_tbl))
symbol date adjusted
VOW.DE 02.06.2014 112.5
VOW.DE 03.06.2014 112.4
VOW.DE 04.06.2014 109.5
VOW.DE 05.06.2014 111.6
VOW.DE 06.06.2014 111.9
VOW.DE 09.06.2014 112.4

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, the Cross-Sectional T Test (AAR and CAAR) is applied by default, alongside Patell Z, BMP, sign, and rank tests.

# 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:

head(est_task$data_tbl)
#> # A tibble: 4 × 5
#> # Groups:   event_id, group, firm_symbol [4]
#>   firm_symbol event_id group    data               request         
#>   <chr>          <int> <chr>    <list>             <list>          
#> 1 VOW.DE             1 VW Group <tibble [360 × 4]> <tibble [1 × 6]>
#> 2 PAH3.DE            2 VW Group <tibble [360 × 4]> <tibble [1 × 6]>
#> 3 BMW.DE             3 Other    <tibble [360 × 4]> <tibble [1 × 6]>
#> 4 MBG.DE             4 Other    <tibble [360 × 4]> <tibble [1 × 6]>
es_tt(head(est_task$data_tbl$data[[1]]))
date firm_adjusted index_symbol index_adjusted
02.06.2014 112.5 ^GDAXI 9950
03.06.2014 112.4 ^GDAXI 9920
04.06.2014 109.5 ^GDAXI 9927
05.06.2014 111.6 ^GDAXI 9948
06.06.2014 111.9 ^GDAXI 9987
09.06.2014 112.4 ^GDAXI 10009 
es_tt(est_task$data_tbl$request[[1]])
index_symbol event_date event_window_start event_window_end shift_estimation_window estimation_window_length
^GDAXI 18.09.2015 -10 10 -11 250

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)
es_tt(head(est_task$data_tbl$data[[1]]))
date firm_adjusted index_symbol index_adjusted firm_returns index_returns event_date relative_index event_window estimation_window
02.06.2014 112.5 ^GDAXI 9950 NA NA 0 -329 0 0
03.06.2014 112.4 ^GDAXI 9920 -0.001284 -0.0030579 0 -328 0 0
04.06.2014 109.5 ^GDAXI 9927 -0.026287 0.0006983 0 -327 0 0
05.06.2014 111.6 ^GDAXI 9948 0.019586 0.0021294 0 -326 0 0
06.06.2014 111.9 ^GDAXI 9987 0.00284 0.0039489 0 -325 0 0
09.06.2014 112.4 ^GDAXI 10009 0.003861 0.0021444 0 -324 0 0 
est_task = fit_model(est_task, param_set)
head(est_task$data_tbl)
#> # A tibble: 4 × 6
#> # Groups:   event_id, group, firm_symbol [4]
#>   firm_symbol event_id group    data                request          model     
#>   <chr>          <int> <chr>    <list>              <list>           <list>    
#> 1 VOW.DE             1 VW Group <tibble [360 × 11]> <tibble [1 × 6]> <MarktMdl>
#> 2 PAH3.DE            2 VW Group <tibble [360 × 11]> <tibble [1 × 6]> <MarktMdl>
#> 3 BMW.DE             3 Other    <tibble [360 × 11]> <tibble [1 × 6]> <MarktMdl>
#> 4 MBG.DE             4 Other    <tibble [360 × 11]> <tibble [1 × 6]> <MarktMdl>
est_task$data_tbl$model[[1]]
#> <MarketModel>
#>   Inherits from: <ModelBase>
#>   Public:
#>     abnormal_returns: function (data_tbl) 
#>     clone: function (deep = FALSE) 
#>     degenerate_mode: lenient
#>     event_id: 1
#>     firm_symbol: VOW.DE
#>     fit: function (data_tbl) 
#>     formula: formula
#>     hac_lag: NULL
#>     initialize: function (use_hac = FALSE, hac_lag = NULL) 
#>     is_fitted: active binding
#>     model: active binding
#>     model_name: MarketModel
#>     set_formula: function (formula) 
#>     statistics: active binding
#>     use_hac: FALSE
#>   Private:
#>     .degenerate_handled: FALSE
#>     .error: NULL
#>     .fitted_model: lm
#>     .is_fitted: TRUE
#>     .statistics: list
#>     add_residuals: function (residuals) 
#>     calculate_forecast_error_correction: function (sigma, estimation_window_length, estimation_market_returns, 
#>     calculate_statistics: function (data_tbl) 
#>     first_order_autocorrelation: function (residuals)
est_task = calculate_statistics(est_task, param_set)
head(est_task$data_tbl)
#> # A tibble: 4 × 8
#> # Groups:   event_id, group, firm_symbol [4]
#>   firm_symbol event_id group    data     request  model      ART      CART    
#>   <chr>          <int> <chr>    <list>   <list>   <list>     <list>   <list>  
#> 1 VOW.DE             1 VW Group <tibble> <tibble> <MarktMdl> <tibble> <tibble>
#> 2 PAH3.DE            2 VW Group <tibble> <tibble> <MarktMdl> <tibble> <tibble>
#> 3 BMW.DE             3 Other    <tibble> <tibble> <MarktMdl> <tibble> <tibble>
#> 4 MBG.DE             4 Other    <tibble> <tibble> <MarktMdl> <tibble> <tibble>
es_tt(est_task$data_tbl$ART[[1]])
#> Warning in `[<-.data.frame`(`*tmp*`, idx, col, value =
#> structure(list(structure(list(: 21 Variablen angegeben, um 1 Variablen zu
#> ersetzen
relative_index abnormal_returns ar_t ar_t_dist
-10 0.0028169 0.28098 248
-9 0.0003573 0.03564 0
-8 0.0091997 0.91765 1
-7 0.0222041 2.21482 NA
-6 -0.0057857 -0.57712 248
-5 0.0058288 0.58141 0
-4 -0.0049238 -0.49114 1
-3 0.0025836 0.2577 NA
-2 0.0002132 0.02127 248
-1 -0.0003757 -0.03748 0
0 -0.0026243 -0.26177 1
1 -0.1910336 -19.05523 NA
2 -0.1418452 -14.14879 248
3 0.0626856 6.25277 0
4 0.0215244 2.14702 1
5 -0.0576803 -5.7535 NA
6 -0.0523001 -5.21684 248
7 -0.0318594 -3.17791 0
8 -0.0074962 -0.74773 1
9 0.0185153 1.84686 NA
10 -0.0423404 -4.22337 248 
es_tt(est_task$data_tbl$CART[[1]])
#> Warning in `[<-.data.frame`(`*tmp*`, idx, col, value =
#> structure(list(structure(list(: 21 Variablen angegeben, um 1 Variablen zu
#> ersetzen
relative_index abnormal_returns event_window_length car_window car corrected_car car_t car_t_dist
-10 0.0028169 1 [-10, -10] 0.002817 0.281 0.281 248
-9 0.0003573 2 [-10, -9] 0.003174 0.3166 0.2239 0.002816886
-8 0.0091997 3 [-10, -8] 0.012374 1.2343 0.7126 0.01002526
-7 0.0222041 4 [-10, -7] 0.034578 3.4491 1.7245 NA
-6 -0.0057857 5 [-10, -6] 0.028792 2.872 1.2844 248
-5 0.0058288 6 [-10, -5] 0.034621 3.4534 1.4098 0.002816886
-4 -0.0049238 7 [-10, -4] 0.029697 2.9622 1.1196 0.01002526
-3 0.0025836 8 [-10, -3] 0.032281 3.2199 1.1384 NA
-2 0.0002132 9 [-10, -2] 0.032494 3.2412 1.0804 248
-1 -0.0003757 10 [-10, -1] 0.032118 3.2037 1.0131 0.002816886
0 -0.0026243 11 [-10, 0] 0.029494 2.942 0.887 0.01002526
1 -0.1910336 12 [-10, 1] -0.16154 -16.1133 -4.6515 NA
2 -0.1418452 13 [-10, 2] -0.303385 -30.2621 -8.3932 248
3 0.0626856 14 [-10, 3] -0.240699 -24.0093 -6.4168 0.002816886
4 0.0215244 15 [-10, 4] -0.219175 -21.8623 -5.6448 0.01002526
5 -0.0576803 16 [-10, 5] -0.276855 -27.6158 -6.9039 NA
6 -0.0523001 17 [-10, 6] -0.329155 -32.8326 -7.9631 248
7 -0.0318594 18 [-10, 7] -0.361015 -36.0105 -8.4878 0.002816886
8 -0.0074962 19 [-10, 8] -0.368511 -36.7583 -8.4329 0.01002526
9 0.0185153 20 [-10, 9] -0.349996 -34.9114 -7.8064 NA
10 -0.0423404 21 [-10, 10] -0.392336 -39.1348 -8.5399 248 
est_task$aar_caar_tbl
#> # A tibble: 2 × 4
#> # Groups:   group [2]
#>   group    data                model            CSectT            
#>   <chr>    <list>              <list>           <list>            
#> 1 VW Group <tibble [720 × 13]> <tibble [2 × 3]> <tibble [21 × 10]>
#> 2 Other    <tibble [720 × 13]> <tibble [2 × 3]> <tibble [21 × 10]>
es_tt(est_task$aar_caar_tbl$CSectT[[1]])
relative_index aar n_events n_valid_events n_pos n_neg aar_t caar caar_t car_window
-10
0.0042		 2 2 2 0 3.0734
0.0042		 3.073 [-10, -10]
-9
-0.0012		 2 2 1 1 -0.7737
0.0030		 13.431 [-10, -9]
-8
0.0067		 2 2 2 0 2.6587
0.0096		 3.525 [-10, -8]
-7
0.0158		 2 2 2 0 2.458
0.0254		 2.777 [-10, -7]
-6
-0.0026		 2 2 1 1 -0.8193
0.0228		 3.819 [-10, -6]
-5
0.0045		 2 2 2 0 3.5468
0.0274		 3.771 [-10, -5]
-4
-0.0042		 2 2 0 2 -5.4838
0.0232		 3.57 [-10, -4]
-3
0.0047		 2 2 2 0 2.2139
0.0279		 6.388 [-10, -3]
-2
0.0080		 2 2 2 0 1.0275
0.0359		 10.612 [-10, -2]
-1
-0.0048		 2 2 0 2 -1.0844
0.0310		 29.015 [-10, -1]
0
-0.0014		 2 2 0 2 -1.1908
0.0296		 231.743 [-10, 0]
1
-0.1913		 2 2 0 2 -804.3893
-0.1616		 -1470.036 [-10, 1]
2
-0.1460		 2 2 0 2 -35.5468
-0.3076		 -72.963 [-10, 2]
3
0.0387		 2 2 2 0 1.6183
-0.2689		 -9.549 [-10, 3]
4
0.0177		 2 2 2 0 4.59
-0.2512		 -7.848 [-10, 4]
5
-0.0581		 2 2 0 2 -148.579
-0.3093		 -9.545 [-10, 5]
6
-0.0512		 2 2 0 2 -45.6437
-0.3604		 -11.524 [-10, 6]
7
-0.0189		 2 2 0 2 -1.46
-0.3793		 -20.7 [-10, 7]
8
-0.0070		 2 2 0 2 -12.8002
-0.3863		 -21.723 [-10, 8]
9
0.0099		 2 2 2 0 1.1519
-0.3764		 -14.264 [-10, 9]
10
-0.0458		 2 2 0 2 -13.2138
-0.4222		 -14.142 [-10, 10]

Visualize the Results

The plot_event_study() function returns a ggplot2 object; wrapping it in plotly::ggplotly() converts it to an interactive widget – hover over data points to inspect exact values, zoom into the announcement window, or click legend entries to toggle series.

Cumulative Abnormal Return (CAR)

The chart below shows the cumulative abnormal return (CAR) path with confidence band for the first event in the study (VW, September 2015 announcement).

The CAR is the running sum of abnormal returns from the start of the event window to each day t. Abnormal returns are the firm’s actual returns minus what the market model predicted – in other words, the return in excess of what you would have expected given the index movement that day. At day 0 (the event date) the cumulative sum resets conceptually: positive values above the band reflect unexpectedly good news; negative values below the band reflect unexpectedly bad news.

The shaded region is the confidence band. When the CAR path moves outside the band the abnormal performance is statistically distinguishable from zero – the market is responding to the event beyond chance variation. When the path stays inside the band the evidence of abnormal performance is not strong enough to rule out noise. For the VW Dieselgate event you will notice a pronounced drop right around the announcement window: the CAR falls sharply below the lower bound of the confidence band, confirming a statistically significant negative market reaction to the emissions disclosure.

Use the hover tooltip to read off the exact CAR value and confidence-band limits at any relative day. The zoom and legend-toggle controls let you focus on the announcement window or isolate individual series.

plotly::ggplotly(plot_event_study(est_task, type = "car"))

Cumulative Average Abnormal Return (CAAR)

The next chart shows the cumulative average abnormal return (CAAR) aggregated across all events in the study.

Where the single-event CAR reflects one firm’s reaction, the CAAR averages abnormal returns across all events at each relative day. Averaging smooths out firm-specific noise – idiosyncratic movements that happen to coincide with the event window for a particular firm but are unrelated to the event itself. When a clean CAAR signal emerges (a sustained move outside the confidence band) it reflects a systematic market reaction shared across firms, not just one company’s idiosyncratic move. A narrow confidence band signals that the individual event reactions were consistent with each other; a wide band suggests heterogeneous reactions across the sample.

For the Dieselgate study the CAAR chart summarises the mean market reaction across all four affected automotive companies, providing a portfolio-level view of how the scandal reverberated through the sector.

plotly::ggplotly(plot_event_study(est_task, type = "caar"))

Next Steps

This vignette covered the core market-model pipeline. To go deeper:

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.

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