Introduction

The EventStudy package includes helper functions to download stock price data and Fama-French factor data, pre-formatted for use with EventStudyTask. This eliminates the need for manual data preparation.

The code in this article is shown for illustration purposes and is not executed at build time because it requires a live network connection.

Downloading Stock Data

The download_stock_data() function downloads adjusted close prices from Yahoo Finance. It requires either the tidyquant or quantmod package.

library(EventStudy)

# Download a single stock
aapl <- download_stock_data("AAPL", from = "2023-01-01", to = "2024-12-31")
head(aapl)
#> # A tibble: 6 x 3
#>   symbol date       adjusted
#>   <chr>  <chr>         <dbl>
#> 1 AAPL   02.01.2023     130.
#> 2 AAPL   03.01.2023     126.
#> ...

Multiple Stocks

stocks <- download_stock_data(
  c("AAPL", "MSFT", "GOOGL", "AMZN"),
  from = "2023-01-01",
  to = "2024-12-31"
)

Format for EventStudyTask

When format_for_task = TRUE (default), dates are converted to the dd.mm.yyyy format required by EventStudyTask:

# Formatted for direct use
firm_data <- download_stock_data(
  c("AAPL", "MSFT"),
  from = "2022-01-01",
  format_for_task = TRUE
)

# Download index data
index_data <- download_stock_data(
  "^GSPC",  # S&P 500
  from = "2022-01-01",
  format_for_task = TRUE
)

# Create the task directly
request <- tibble::tibble(
  event_id = 1:2,
  firm_symbol = c("AAPL", "MSFT"),
  index_symbol = "^GSPC",
  event_date = c("15.06.2023", "15.06.2023"),
  group = "Tech",
  event_window_start = -5,
  event_window_end = 5,
  shift_estimation_window = -6,
  estimation_window_length = 120
)

task <- EventStudyTask$new(firm_data, index_data, request)

Raw Format

Set format_for_task = FALSE to get the original data without date conversion:

raw_data <- download_stock_data("AAPL", from = "2023-01-01",
                                  format_for_task = FALSE)

Downloading Factor Data

The download_factor_data() function downloads Fama-French factor data directly from the Kenneth French Data Library.

Fama-French 3-Factor

ff3 <- download_factor_data(model = "ff3", frequency = "daily")
head(ff3)
#> # A tibble: 6 x 5
#>   date       market_excess     smb     hml risk_free_rate
#>   <chr>              <dbl>   <dbl>   <dbl>          <dbl>
#> 1 03.01.2023        0.0112  0.0023 -0.0045         0.0002
#> ...

Fama-French 5-Factor

ff5 <- download_factor_data(model = "ff5", frequency = "daily")
# Includes: market_excess, smb, hml, rmw, cma, risk_free_rate

Monthly Frequency

ff3_monthly <- download_factor_data(model = "ff3", frequency = "monthly")

Using with Factor Models

# Download data
firm_data <- download_stock_data(c("AAPL", "MSFT"), from = "2022-01-01")
index_data <- download_stock_data("^GSPC", from = "2022-01-01")
factor_tbl <- download_factor_data(model = "ff3", frequency = "daily")

# Create task with factor data
task <- EventStudyTask$new(firm_data, index_data, request,
                            factor_tbl = factor_tbl)

# Use a factor model
ps <- ParameterSet$new(
  return_model = FamaFrench3FactorModel$new()
)
task <- run_event_study(task, ps)

Downloading Risk-Free Rate

The download_risk_free_rate() function extracts the risk-free rate from the FF3 data:

rf <- download_risk_free_rate(frequency = "daily")
head(rf)
#> # A tibble: 6 x 2
#>   date       risk_free_rate
#>   <chr>               <dbl>
#> 1 03.01.2023         0.0002
#> ...

Column Name Mapping

When format_for_task = TRUE, factor data columns are automatically renamed:

Original	Renamed
Mkt-RF	market_excess
SMB	smb
HML	hml
RMW	rmw
CMA	cma
Mom	mom
RF	risk_free_rate

Downloading Stock and Factor Data

Simon Mueller

2026-10-09