Event Study Analysis in R

A comprehensive, modular R package for financial event study analysis. Implements the classical methodology (MacKinlay 1997) and extends it with modern multi-factor models, long-horizon methods, panel (DiD) event studies, and more.

Installation

GitHub

install.packages("devtools")
devtools::install_github("sipemu/eventstudy")

Features

• 13 Return Models: Market Model, Market Adjusted, Mean Adjusted, Fama-French 3- and 5-factor, Carhart 4-factor, GARCH(1,1), Buy-and-Hold Abnormal Returns (BHAR), Volume, and Volatility models.

• 11 Test Statistics: Parametric (AR T, CAR T, BHAR T, Cross-Sectional T, Patell Z, BMP) and non-parametric (Sign, Generalized Sign, Rank, Calendar-Time Portfolio).

• Diagnostics: Shapiro-Wilk normality, Durbin-Watson, Ljung-Box autocorrelation, pre-trend testing.

• Visualization: Event study plots (AR, CAR, AAR, CAAR) with confidence bands, diagnostic plots, CAR distribution histograms, panel event study plots.

• Result Export: CSV, Excel (multi-sheet), and LaTeX (publication-ready tables). Broom-compatible tidy() method for tidyverse workflows.

• Cross-Sectional Analysis: Regress CARs on firm characteristics with HC-robust standard errors. Group comparisons, quantiles, and distribution plots.

• Intraday Event Studies: POSIXct timestamp support with minute/second-level windows.

• Panel Event Studies (DiD): Two-way fixed effects (static and dynamic), Sun & Abraham (2021) interaction-weighted estimator for staggered treatment.

• Extensible Design: Add custom models (inherit ModelBase) and test statistics (inherit TestStatisticBase).

Quick Start

library(EventStudy)

# Create task from your data
task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)

# Run with default settings (Market Model, simple returns, all test statistics)
task <- run_event_study(task)

# Inspect results
print(task)
summary(task)

Example: Dieselgate

Analyzing the 2015 Volkswagen emissions scandal and its impact on automotive stock prices.

1. Load Data

library(tidyquant)
library(dplyr)
library(EventStudy)

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

request_tbl <- tibble(
  event_id = 1:4,
  firm_symbol = firm_symbols,
  index_symbol = rep(index_symbol, 4),
  event_date = rep("18.09.2015", 4),
  group = c(rep("VW Group", 2), rep("Other", 2)),
  event_window_start = rep(-10, 4),
  event_window_end = rep(10, 4),
  shift_estimation_window = rep(-11, 4),
  estimation_window_length = rep(250, 4)
)

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

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

2. Run the Event Study

One-liner with defaults:

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)
task <- run_event_study(task)

Step-by-step with custom parameters:

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)

est_params <- ParameterSet$new(
  return_calculation = LogReturn$new(),
  return_model = MarketModel$new(),
  single_event_statistics = SingleEventStatisticsSet$new(),
  multi_event_statistics = MultiEventStatisticsSet$new()
)

task <- prepare_event_study(task, est_params)
task <- fit_model(task, est_params)
task <- calculate_statistics(task, est_params)

Pipe-friendly:

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)
est_params <- ParameterSet$new(return_calculation = LogReturn$new())

task |>
  prepare_event_study(est_params) |>
  fit_model(est_params) |>
  calculate_statistics(est_params)

3. Extract Results

# Overview
print(task)
summary(task)

# Single-event results
task$get_ar(event_id = 1)           # Abnormal returns
task$get_car(event_id = 1)          # Cumulative abnormal returns
task$get_model_stats(event_id = 1)  # Model fit statistics

# Multi-event results
task$get_aar(stat_name = "CSectT")  # AAR/CAAR table

# Broom-compatible tidy output
tidy.EventStudyTask(task, type = "ar")
tidy.EventStudyTask(task, type = "car")
tidy.EventStudyTask(task, type = "aar")
tidy.EventStudyTask(task, type = "model")

4. Export Results

# CSV (one file per result type)
export_results(task, "results.csv")

# Excel (each result type on a separate sheet)
export_results(task, "results.xlsx")

# LaTeX (publication-ready tables)
export_results(task, "results.tex", which = c("model", "aar"))

5. Visualize

# Event study plots with confidence bands
plot_event_study(task, type = "car", event_id = 1)
plot_event_study(task, type = "caar", group = "VW Group")

# Model diagnostics
plot_diagnostics(task, event_id = 1)

# CAR distribution across events
plot_car_distribution(task, by_group = TRUE)

# Raw price plots
plot_stocks(task, add_event_date = TRUE)

6. Diagnostics and Validation

validate_task(task)
model_diagnostics(task)
pretrend_test(task)

Available Models

Model	Class	Description
Market Model	MarketModel	OLS: $R_i = \alpha + \beta R_m + \epsilon$
Market Adjusted	MarketAdjustedModel	$AR_i = R_i - R_m$
Mean Adjusted	ComparisonPeriodMeanAdjustedModel	$AR_i = R_i - \bar{R}_i^{est}$
Fama-French 3-Factor	FamaFrench3FactorModel	$R_i - R_f = \alpha + \beta_m(R_m - R_f) + \beta_s SMB + \beta_h HML + \epsilon$
Fama-French 5-Factor	FamaFrench5FactorModel	3-factor + RMW + CMA
Carhart 4-Factor	Carhart4FactorModel	3-factor + MOM
GARCH(1,1)	GARCHModel	Time-varying volatility (requires rugarch)
BHAR	BHARModel	Buy-and-hold: $\prod(1+R_i) - \prod(1+R_m)$
Volume	VolumeModel	Abnormal trading volume
Volatility	VolatilityModel	Abnormal volatility (variance ratio)
Custom	CustomModel	Extend MarketModel with custom AR logic
Linear Factor	LinearFactorModel	Base class for all OLS factor models

Using Factor Models

Factor models require a factor_tbl with Fama-French data:

factor_data <- tibble(
  date = dates,             # matching format as firm/index data
  smb = smb_returns,
  hml = hml_returns,
  risk_free_rate = rf_rate  # triggers automatic excess return computation
)

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl,
                            factor_tbl = factor_data)
params <- ParameterSet$new(return_model = FamaFrench3FactorModel$new())
task <- run_event_study(task, params)

Available Test Statistics

Single Event (per-firm AR/CAR)

Test	Class	Description
AR T-Test	ARTTest	$t = AR_{i,t} / \sigma_i$
CAR T-Test	CARTTest	$t = CAR_i / (\sqrt{L} \cdot \sigma_i)$
BHAR T-Test	BHARTTest	t-test on buy-and-hold abnormal returns

Multiple Events (cross-sectional AAR/CAAR)

Test	Class	Description
Cross-Sectional T	CSectTTest	Standard cross-sectional t-test
Patell Z	PatellZTest	Standardized residual test (Patell 1976)
BMP Test	BMPTest	Standardized cross-sectional (Boehmer et al. 1991)
Sign Test	SignTest	Proportion of positive ARs vs 50%
Generalized Sign	GeneralizedSignTest	Adjusted for asymmetry (Cowan 1992)
Rank Test	RankTest	Non-parametric rank test (Corrado 1989)
Calendar-Time Portfolio	CalendarTimePortfolioTest	Portfolio approach for clustered events

Cross-Sectional Regression

Explain why some firms are affected more than others:

firm_chars <- tibble(
  event_id = 1:4,
  log_market_cap = c(10, 11, 12, 10.5),
  leverage = c(0.3, 0.5, 0.2, 0.4)
)

result <- cross_sectional_regression(
  task, formula = ~ log_market_cap + leverage,
  data = firm_chars
)
print(result)

# Group comparisons
car_by_group(task)
car_quantiles(task)

Intraday Event Studies

For high-frequency studies around earnings announcements, Fed decisions, etc.:

task <- IntradayEventStudyTask$new(
  firm_intraday_tbl,    # symbol, timestamp (POSIXct), price
  index_intraday_tbl,
  request_tbl           # event_timestamp, windows in observation counts
)
task <- prepare_intraday_event_study(task, ParameterSet$new())
task <- fit_model(task, ParameterSet$new())

Non-parametric significance test (Rinaudo & Saha 2014): compares event-day CARs to the empirical distribution of estimation-day CARs to build confidence bands without distributional assumptions.

results <- nonparametric_intraday_test(
  estimation_window = est_data,   # day, time, abnormalReturn
  event_window      = event_data, # time, abnormalReturn
  event_times       = c("10:00", "14:30"),
  p = 0.05
)

See vignette("intraday-event-study") for details.

Panel Event Studies (DiD)

For difference-in-differences style analysis with staggered treatment:

task <- PanelEventStudyTask$new(
  panel_data,
  unit_id = "firm_id",
  time_id = "year",
  outcome = "revenue",
  treatment = "policy_adopted",
  treatment_time = "adoption_year"
)

# Dynamic TWFE with event study plot
task <- estimate_panel_event_study(task, method = "dynamic_twfe",
                                    leads = 5, lags = 5)
plot_panel_event_study(task)

# Sun & Abraham for staggered treatment
task <- estimate_panel_event_study(task, method = "sun_abraham",
                                    leads = 5, lags = 5)

Extending the Package

Custom Models

MyModel <- R6::R6Class("MyModel",
  inherit = ModelBase,
  public = list(
    model_name = "MyModel",
    fit = function(data_tbl) {
      # Estimate on estimation window, set private$.is_fitted = TRUE
      # Populate private$.statistics$sigma and $degree_of_freedom
    },
    abnormal_returns = function(data_tbl) {
      # Return data_tbl with an abnormal_returns column
    }
  )
)

Custom Test Statistics

MyTest <- R6::R6Class("MyTest",
  inherit = TestStatisticBase,
  public = list(
    name = "MyTest",
    compute = function(data_tbl, model) {
      # Return a tibble with your test results
    }
  )
)

multi_tests <- MultiEventStatisticsSet$new()
multi_tests$add_test(MyTest$new())

See vignette("custom-models") and vignette("custom-test-statistics") for detailed tutorials.

Vignettes

Vignette	Topic
introduction	Package overview with Dieselgate example
custom-models	Creating and using custom return models
custom-test-statistics	Creating custom test statistics
result-extraction	Extracting and exporting results
factor-models-bhar	Factor models (FF3, FF5, Carhart), GARCH, and BHAR
cross-sectional-analysis	Cross-sectional regression, group comparisons, CAR quantiles
diagnostics-validation	Data validation, model diagnostics, pre-trend testing
volume-volatility-event-study	Volume and volatility event studies
intraday-event-study	Non-parametric intraday event studies
panel-event-study	Panel DiD event studies

Roadmap

• Return models (Market, Market Adjusted, Mean Adjusted)
• Parametric test statistics (AR T, CAR T, CSect T, Patell Z, BMP)
• Non-parametric test statistics (Sign, Generalized Sign, Rank)
• Diagnostics and pre-trend testing
• Event study visualization (AR, CAR, AAR, CAAR)
• Task validation
• Export to CSV, Excel, LaTeX
• Broom-compatible tidy() method
• Fama-French 3-factor, 5-factor, Carhart 4-factor models
• GARCH(1,1) model
• Buy-and-hold abnormal returns (BHAR)
• Calendar-time portfolio test
• Volume and volatility event studies
• Cross-sectional regression analysis
• Intraday event study support
• Panel event study module (static/dynamic TWFE, Sun & Abraham 2021)
• Vignettes for custom models, test statistics, result extraction, panel
• CRAN submission

References

• MacKinlay, A. C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35(1), 13-39.
• Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
• Fama, E. F. & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
• Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
• Boehmer, E., Musumeci, J. & Poulsen, A. B. (1991). Event-study methodology under conditions of event-induced variance. Journal of Financial Economics, 30(2), 253-272.
• Corrado, C. J. (1989). A nonparametric test for abnormal security-price performance in event studies. Journal of Financial Economics, 23(2), 385-395.
• Cowan, A. R. (1992). Nonparametric event study tests. Review of Quantitative Finance and Accounting, 2(4), 343-358.
• Patell, J. M. (1976). Corporate forecasts of earnings per share and stock price behavior. Journal of Accounting Research, 14(2), 246-276.
• Rinaudo, J. B. & Saha, A. (2014). Non-parametric intraday event studies.
• Sun, L. & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.

License

AGPL-3