Event Study Analysis Made Simple
Professional tools for measuring the impact of events on financial markets. From Google Sheets templates to a comprehensive R package with 15 return models and 12 test statistics, and an in-browser web app powered by WebAssembly.
Our Tools
Everything You Need for Event Studies
Capabilities
Comprehensive Event Study Framework
15 Expected Return Models
Market, Fama-French 3/5, Carhart, GARCH, and more. Choose the model that best fits your research design.
12 Test Statistics
Parametric and non-parametric tests for single and multi-event studies, including bootstrap inference.
Panel Event Studies
6 DiD estimators including TWFE, Callaway-Sant'Anna, Sun-Abraham, and more for staggered treatments.
Synthetic Control
Counterfactual estimation with placebo tests for single-unit case studies.
Intraday Analysis
Minute-level precision for high-frequency events. Study market reactions within the trading day.
Power Analysis
Monte Carlo simulation for study design. Determine the sample size and event window you need.
Quick Start
Three Steps to Your Event Study
Prepare Your Data
Organize your event dates, firm identifiers, and stock prices. Works with any market data source.
Choose Your Model
Select from 15 return models and configure test statistics to match your research design.
Analyze Results
Get abnormal returns, test statistics, and publication-ready plots for your paper or report.
See It in Action
Simple Yet Powerful R Interface
library(EventStudy)
# Single-event study
result <- event_study(
data = stock_data,
event_date = "2024-01-15",
firm_id = "AAPL",
model = "market_model",
est_window = c(-250, -11),
event_window = c(-10, 10)
)
# View results
summary(result)
plot(result, type = "car")Learn
Master Event Study Methodology
Ready to Start Your Event Study?
Get started with our free Google Sheets template or dive into the R package.