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 Studi