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Earnings Surprise Multi-Firm Event Study Dataset

Source: R/data-earnings-surprises.R
earnings_surprises.Rd

A small, frozen dataset bundling daily prices for three U.S. large-cap firms and the S&P 500 benchmark around the first-calendar-quarter 2023 (Jan-Mar) earnings announcements, reported late April / early May 2023, ready to drive a complete event study pipeline (prepare_event_study() -> fit_model() -> calculate_statistics()).

Usage

data(earnings_surprises)

Format

A named list with four elements:

firm

A tibble of daily prices for all three firms combined, with columns symbol (one of "AAPL", "MSFT", "GOOGL"), date (character, "%d.%m.%Y" format), and adjusted (numeric adjusted close). Rows for all firms are stacked (813 rows total).

index

A tibble of S&P 500 ("^GSPC") daily prices with the same symbol / date / adjusted columns, used as the benchmark for all three events.

request

A three-row tibble giving the event-study request specifications, one row per firm, with the nine columns expected by EventStudyTask: event_id (1L to 3L), firm_symbol, index_symbol, event_date ("04.05.2023" for AAPL, "25.04.2023" for MSFT and GOOGL), group ("Earnings Beat" for all firms), event_window_start (-5), event_window_end (5), shift_estimation_window (-6), and estimation_window_length (200).

meta

A list of provenance metadata: firm_tickers, index_ticker, event_dates, from, to, source, access_date, and note.

Source

Yahoo Finance daily adjusted prices, retrieved 2026-09-05 via the package's own download_stock_data. This is a small illustrative sample bundled for academic / demonstration use only; see data-raw/earnings_surprises.R for the reproducible fetch script.

Details

All three firms beat consensus EPS estimates for their respective January-to-March 2023 quarters: Apple Inc. reported on 2023-05-04 (its fiscal Q2 FY2023, which runs Jan-Mar) and beat by approximately 8%, driving a strong next-day return; Microsoft Corporation reported on 2023-04-25 (its fiscal Q3 FY2023, which runs Jan-Mar), beating cloud (Azure) estimates with a roughly +7% next-day response; Alphabet Inc. reported on 2023-04-25 (Q1 CY2023, Jan-Mar), with advertising revenue exceeding expectations. The bundled window layout uses a 200-trading-day estimation window ending 6 days before each event and an event window of [-5, +5] trading days. Running a market model on this single-group panel produces a positive cumulative average abnormal return (CAAR) over the event window, reflecting the shared earnings-beat signal across all three firms.

Firms:

  • AAPL — Apple Inc. (NASDAQ), event_id = 1

  • MSFT — Microsoft Corporation (NASDAQ), event_id = 2

  • GOOGL — Alphabet Inc. Class A (NASDAQ), event_id = 3

Group: "Earnings Beat" (all firms). Benchmark: S&P 500 index (ticker ^GSPC). Date range: 2022-06-01 to 2023-06-30.

Examples

# \donttest{
data(earnings_surprises)

# Build task and run the full pipeline
task <- EventStudyTask$new(earnings_surprises$firm,
                           earnings_surprises$index,
                           earnings_surprises$request)
task <- run_event_study(task, ParameterSet$new())

# Multi-event: AAR/CAAR across all firms
caar_tbl <- task$aar_caar_tbl$CSectT[[1]]
tail(caar_tbl[, c("relative_index", "caar", "caar_t")], 1)
#> # A tibble: 1 × 3
#>   relative_index   caar caar_t
#>            <int>  <dbl>  <dbl>
#> 1              5 0.0377   2.35
# }