## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----load-libraries, message=FALSE, warning=FALSE-----------------------------
# # Install required packages if not already installed:
# # install.packages(c("yahoofinancer", "dplyr", "tidyr", "purrr", "ggplot2", "scales"))
# 
# library(yahoofinancer)
# library(dplyr)
# library(tidyr)
# library(purrr)
# library(ggplot2)
# library(scales)

## ----define-events------------------------------------------------------------
# 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")
# )

## ----fetch-single-event-------------------------------------------------------
# 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

## ----compute-ar---------------------------------------------------------------
# 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)
# }

## ----inspect-ar---------------------------------------------------------------
# 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

## ----plot-daily-ar------------------------------------------------------------
# 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)

## ----car-across-events--------------------------------------------------------
# car_paths <- map2(events$label, events$event_date, compute_ar) |>
#   list_rbind() |>
#   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")

## ----plot-car-----------------------------------------------------------------
# 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)

## ----full-workflow------------------------------------------------------------
# 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(events$label, events$event_date, compute_ar) |>
#   list_rbind() |>
#   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)