event-study-cars

Event Study: Cumulative Abnormal Returns (CARs)

A complete methodology reference for computing publication-quality CARs with robust test statistics, matching the rigor of Kaspereit's eventstudy2 (v3.2b) for Stata. This skill is generic — applicable to any market, asset class, or event type.

Use the shipped engine first (do not rewrite it)

scripts/eventstudy.py is a complete, runnable Python replication of eventstudy2, validated against the Stata package to floating-point precision (AR ~1e-8, CAR ~6e-8, CAAR and the implemented test statistics ~1e-7) on a generic CRSP sample across all four models (FM, COMEAN, MA, RAW). It is generic — all column names, the model, windows, thin-trading, and log handling are CLI flags. When a user wants CARs computed, run this engine; do not author a new pipeline.

python scripts/eventstudy.py --selftest          # synthetic self-check, no inputs
python scripts/eventstudy.py \
    --returns returns.csv --market market.csv --events events.csv \
    --id-col permno --ret-col ret --event-date-col event_date --mkt-col vwretd \
    --model FM --car-windows "-1,1;-5,5;-10,10" \
    --eswlb -250 --eswub -30 --evwlb -10 --evwub 10 --out-dir out/

Inputs are CSV/Parquet: returns (id, date, ret), market/factors (date, mkt[, factors]), events (id, event_date). Outputs: ar_panel.csv, car_panel.csv, test_statistics.csv. Requires numpy/pandas/scipy. Run --help for all flags (--factor-cols smb,hml, --model MA, --no-thin-trading, ...). The sections below document the methodology the engine implements; read them to audit, extend, or port it.

Methodology Overview: The 8-Step Pipeline

Step 1: Build Trading Calendar (Dateline)

Construct a master list of valid trading dates from the security returns file.

1. Collect all unique dates on which at least one security has a non-missing return (or, if using a factor model, dates where market/factor returns exist).
2. Count the number of securities with valid returns on each date.
3. Optionally drop weekends (delweekend).
4. Apply dateline_threshold: drop dates where the count of return observations falls below threshold × mean(daily_count). A threshold of 0.2 works well for international samples with heterogeneous holidays.
5. The resulting date vector is the dateline — all downstream windows are defined in dateline time (relative trading days), not calendar time.