Event Study: Earnings Announcements and Price Reactions • yahoofinancer

Overview

In this guide, you will run a miniature event study: measure how a stock reacts to its own earnings announcements, after stripping out the market’s general drift. The workflow is the standard one from the event-study literature, simplified to the market-adjusted model:

Pick the announcement dates (the events).
Download price history for the stock and a benchmark index around each date.
Compute daily returns; the abnormal return ( $AR_t$ ) is the stock’s return minus the benchmark’s return on the same day.
Accumulate $AR_t$ across an event window (here, 2 days before to 5 days after the announcement) to obtain the cumulative abnormal return (CAR).

(Note: A full event study would estimate a stock-specific beta over a pre-event estimation window and test CARs for statistical significance. The market-adjusted shortcut shown here assumes beta ≈ 1—fine for exploration and visualization, but treat any inference with care.)

Required Packages

# Install required packages if not already installed:
# install.packages(c("dplyr", "tidyr", "purrr", "ggplot2", "scales"))

library(yahoofinancer)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(scales)

1. Define Events and Fetch Windows

We study three recent Apple earnings announcements. For each, we will download two calendar weeks on either side—enough slack that the ±2/+5 trading-day window always fits inside the fetched range:

events <- tibble::tribble(
  ~label,     ~event_date,
  "FY25 Q4",  as.Date("2025-10-30"),
  "FY26 Q1",  as.Date("2026-01-29"),
  "FY26 Q2",  as.Date("2026-04-30")
)

(Note: Announcement dates are public knowledge—check Apple’s investor relations page for your period of interest and adjust freely.)

Fetching both symbols in one call keeps the benchmark perfectly aligned with the stock’s calendar:

aapl_event <- yf_download_prices(
  tickers  = c("AAPL", "^GSPC"),
  start    = as.Date("2026-01-15"),
  end      = as.Date("2026-02-12"),
  interval = "1d"
)

head(aapl_event, n = 4)
#> # A tibble: 4 x 8
#>   symbol date                 open  high   low close adj_close  volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>   <dbl>
#> 1 ^GSPC  2026-01-15 14:30:00 6890. 6920. 6880. 6905.     6905. 4.90e9
#> 2 AAPL   2026-01-15 14:30:00  231.  232.  230.  231.      230. 4.51e7
#> 3 ^GSPC  2026-01-16 14:30:00 6905. 6935. 6899. 6928.     6928. 5.12e9
#> 4 AAPL   2026-01-16 14:30:00  231.  233.  230.  232.      231. 4.13e7

2. Compute Abnormal Returns

The helper below pivots each window to one row per day with side-by-side price columns, computes daily returns for both series, aligns every row to the announcement day (rel_day 0), and keeps the event window:

compute_ar <- function(label, event_date) {
  raw <- yf_download_prices(
    tickers  = c("AAPL", "^GSPC"),
    start    = event_date - 14,
    end      = event_date + 14,
    interval = "1d"
  )

  raw |>
    mutate(symbol = if_else(symbol == "^GSPC", "index", symbol)) |>
    select(date, symbol, close) |>
    pivot_wider(names_from = symbol, values_from = close) |>
    arrange(date) |>
    mutate(
      ret_stock = AAPL / lag(AAPL) - 1,
      ret_mkt   = index / lag(index) - 1,
      ar        = ret_stock - ret_mkt
    ) |>
    # Locate the first session on/after the announcement (t = 0)
    mutate(rel_day = row_number() - which(as.Date(date) >= event_date)[1]) |>
    filter(rel_day %in% -2:5) |>
    transmute(label, rel_day, ar)
}

Running it for a single event shows the shape of the result—the announcement reaction lands at rel_day 0 or 1 depending on whether the release came before or after the bell:

ar_q1 <- compute_ar("FY26 Q1", as.Date("2026-01-29"))

ar_q1
#> # A tibble: 8 x 3
#>   label   rel_day       ar
#>   <chr>      <int>    <dbl>
#> 1 FY26 Q1       -2 -0.0042
#> 2 FY26 Q1       -1  0.0019
#> 3 FY26 Q1        0  0.0031
#> 4 FY26 Q1        1  0.0384
#> 5 FY26 Q1        2  0.0057
#> # i 3 more rows

3. Daily Abnormal Returns Around One Announcement

A diverging bar chart makes the announcement-day pop obvious:

ggplot(ar_q1, aes(x = rel_day, y = ar, fill = ar >= 0)) +
  geom_col(width = 0.7, show.legend = FALSE) +
  scale_fill_manual(values = c("#c62828", "#2e7d32")) +
  scale_x_continuous(breaks = -2:5) +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  labs(
    title    = "AAPL Abnormal Returns — FY26 Q1 Earnings",
    subtitle = "Market-adjusted vs. S&P 500; day 0 = announcement",
    x        = "Trading days relative to announcement",
    y        = "Abnormal return",
    caption  = "Source: Yahoo Finance via yahoofinancer"
  ) +
  theme_minimal(base_size = 12)

4. Average CAR Across All Events

Now run the helper over every event with map2_dfr() and accumulate: thin lines show each announcement’s CAR path, the thick line their average:

car_paths <- map2_dfr(events$label, events$event_date, compute_ar) |>
  group_by(label) |>
  arrange(rel_day, .by_group = TRUE) |>
  mutate(car = cumsum(replace_na(ar, 0))) |>
  ungroup()

avg_car <- car_paths |>
  group_by(rel_day) |>
  summarise(car = mean(car), .groups = "drop")

(Note: arrange(rel_day, .by_group = TRUE) sorts within each event so the running sum never crosses event boundaries; replace_na() guards the leading NA return.)

ggplot() +
  geom_line(data = car_paths,
            aes(x = rel_day, y = car, group = label),
            color = "#9aa5ad", linewidth = 0.5) +
  geom_line(data = avg_car, aes(x = rel_day, y = car),
            color = "#1f77b4", linewidth = 1.2) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "#555555") +
  scale_x_continuous(breaks = -2:5) +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  labs(
    title    = "Average Cumulative Abnormal Return — AAPL Earnings",
    subtitle = "Grey: individual announcements; blue: average",
    x        = "Trading days relative to announcement",
    y        = "Cumulative abnormal return",
    caption  = "Source: Yahoo Finance via yahoofinancer"
  ) +
  theme_minimal(base_size = 12)

5. Reading the Results

A steep jump at day 0/1 means the announcement surprised the market relative to the index; a flat path implies results were priced in.
Drift after day 1 (continuation) versus reversal speaks to post-earnings-announcement behavior—compare across many quarters before believing either pattern.
Caveats: single-stock CARs conflate firm news with sector news (the S&P 500 is an imperfect control); without significance testing, treat magnitudes as descriptive only. The cookbook’s rolling-correlation recipe (vignette("cookbook", package = "yahoofinancer")) is a natural next step toward proper factor controls.

6. Minimal Reproducible Example

Below is the complete, self-contained workflow in a single copy-pasteable script:

library(yahoofinancer)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(scales)

# 1. Events: three earnings announcements, two weeks of slack each side
events <- tibble::tribble(
  ~label,    ~event_date,
  "FY25 Q4", as.Date("2025-10-30"),
  "FY26 Q1", as.Date("2026-01-29"),
  "FY26 Q2", as.Date("2026-04-30")
)

# 2. Market-adjusted abnormal returns over [-2, +5]
compute_ar <- function(label, event_date) {
  yf_download_prices(c("AAPL", "^GSPC"),
                     start = event_date - 14,
                     end   = event_date + 14) |>
    mutate(symbol = if_else(symbol == "^GSPC", "index", symbol)) |>
    select(date, symbol, close) |>
    pivot_wider(names_from = symbol, values_from = close) |>
    arrange(date) |>
    mutate(ret_stock = AAPL / lag(AAPL) - 1,
           ret_mkt   = index / lag(index) - 1,
           ar        = ret_stock - ret_mkt) |>
    mutate(rel_day = row_number() - which(as.Date(date) >= event_date)[1]) |>
    filter(rel_day %in% -2:5) |>
    transmute(label, rel_day, ar)
}

# 3. Cumulate per event and average across events
avg_car <- map2_dfr(events$label, events$event_date, compute_ar) |>
  group_by(label) |>
  arrange(rel_day, .by_group = TRUE) |>
  mutate(car = cumsum(replace_na(ar, 0))) |>
  ungroup() |>
  group_by(rel_day) |>
  summarise(car = mean(car), .groups = "drop")

# 4. Plot the average CAR path
ggplot(avg_car, aes(x = rel_day, y = car)) +
  geom_line(color = "#1f77b4", linewidth = 1.2) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "#555555") +
  scale_x_continuous(breaks = -2:5) +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  labs(title = "Average CAR — AAPL Earnings Announcements",
       x = "Trading days relative to announcement",
       y = "Cumulative abnormal return") +
  theme_minimal(base_size = 12)

7. Summary

In this guide, you learned how to:

Frame events: Turn announcement dates into bounded download windows with yf_download_prices(start, end).
Align stock and benchmark: Pivot both series onto a shared calendar and difference their returns.
Localize reactions: Anchor each session to the announcement via rel_day and slice the [-2, +5] window.
Accumulate evidence: Build per-event CAR paths (sorting with .by_group = TRUE) and average them into a single readable chart.

8. Going Further

More events: Loop over a year of announcements—or screen other firms—and facet the CAR plots by ticker with facet_wrap(~ symbol).
Intraday precision: Refetch the announcement week at interval = "60m" (max 730-day lookback applies) to separate the opening-gap reaction from same-day drift.
Portfolio context: Compare event-day volume against typical turnover using the OHLCV columns already returned.
More recipes: For drawdown analysis, technical indicators (EMA, RSI, MACD, Bollinger Bands), and portfolio performance modeling, see vignette("cookbook", package = "yahoofinancer").