Event Study

event-study

You hand over an event (a date + a class) and either one ticker or a basket. The skill measures abnormal returns over the event window, compares each reaction to the name's own history, and aggregates across the cross-section when the input is wider than one event.

This is the workflow a quant or event-driven PM runs when asking "did the market actually react to this," "is the cross-section consistent," or "is this kind of event a tradeable signal." The output matches the format an analyst already reads: morning-note style for a single event, screener table for a cross-section, summary stats for an aggregate.

When to invoke

• A PM is sizing into a name post-print and wants to know "what's the T+5 base rate after a beat like this"
• A quant is testing whether dividend hikes (or cuts) lead to measurable abnormal returns across a sector
• A trader saw an unusual volume day on a peer and wants to know whether the event class historically resolves
• The user says "event study on X", "what's the average abnormal return after Y", "did the market price in Z", or "cross-section reaction across mega-cap tech earnings"

Three modes (determined by input shape)

The same code path runs all three; the shape of --tickers and --event-date (vs --window) picks the output mode.

Mode 1: single (single ticker, single event)

Input: --ticker NVDA --event-date 2026-05-20 --event-class earnings

Output: a sell-side note with the event window returns, t-stat of this event's abnormal return vs the name's prior reaction distribution, and a one-line take. Matches the layout of ../earnings-drilldown but generalized to any event class. See references/rendering.md.

Mode 2: cross-section (many tickers, one event period)

Input: --tickers AAPL,NVDA,MSFT,GOOGL,META --event-class earnings --period 2026Q2

Output: a comparison table (one row per ticker), plus a "Cross-section" footer with the average T+5 CAR, the median, the t-stat of the average against zero, and the correlation between surprise magnitude and reaction.

Mode 3: aggregate (many tickers, many events)

Input: --tickers AAPL,NVDA,MSFT,GOOGL,META --event-class earnings --window 2025-06-01..2026-06-24

Output: only the aggregate statistics. Average CAR by horizon (T+1, T+3, T+5), t-stat against zero, percentile distribution, n. No per-event detail in the rendered output (it's in the JSON for UIs). Used for "is this event class a tradeable signal at all" questions.

Event classes supported

Each class has its own resolution helper documented in references/event-class-definitions.md. Adding a new event class is a clean PR: implement the resolver, add a row to the table above, and the skill picks it up.

Out of scope for v1: analyst upgrades/downgrades (the Benzinga analyst-ratings endpoint wasn't reliably reachable in prior sessions; queued for v2), index inclusions/exclusions, M&A announcements.

What you need

• A list of tickers (one or many)
• Either a specific event date or a window
• MASSIVE_API_KEY exported

Tiers:

• Tier A (full fidelity for earnings): Stocks Starter + Benzinga Earnings. True press release dates, consensus, surprise %, allows the "surprise vs reaction" correlation column in cross-section.
• Tier B (degraded earnings): Stocks Starter only. 8-K item 2.02 acceptance date as print date; no surprise %, so the cross-section drops the surprise-vs-reaction correlation and falls back to reaction-sign bucketing. dividend_changes and large_volume_spike run identically on either tier.

What you get back

Two output layers from one analysis.

Layer 1: canonical JSON matching output-schema.json. Discriminated by output_mode: single, cross_section, or aggregate. Each mode exposes the per-subject event_window_returns, abnormal_returns, and t_stat_vs_history. Cross-section and aggregate add the cross-sectional summary block, which includes distribution_shape (KDE-derived n_modes, modality label, tail label, skew, excess kurtosis, sparkline) when n_subjects >= 10 so bimodal or fat-tailed reactions surface instead of hiding behind a benign mean. UIs and downstream agents consume this.

Layer 2: rendered output in hybrid mode:

• single → sell-side note
• cross_section → comparison table + cross-section footer
• aggregate → summary stats block

See references/rendering.md for the full rules.

How it works

The pipeline is the same regardless of input shape; what changes is how the rendering layer collapses the result.

1. Resolve events. Per references/event-class-definitions.md, convert the input (ticker + class + date-or-window) into a list of concrete (ticker, event_date, event_metadata) tuples.
2. Pull daily aggregates for each ticker and SPY across the union of event windows plus a 30-day buffer on either side.
3. Compute abnormal returns per references/abnormal-returns.md. AR = raw_return − SPY_return at each horizon (T0, T+1, T+3, T+5). CAR = sum of ARs from T+1 through the horizon.
4. Compute t-stats per references/t-stat-significance.md. For a single event, compare this event's T+5 CAR to the name's prior reaction distribution. For a cross-section, t-stat is the mean CAR across events vs zero. Both require n≥8 to be reported as significant; below that, the rendered output prints the t-stat but marks it "underpowered."
5. Aggregate cross-sectionally per references/cross-section-methodology.md when n_subjects > 1. Average CAR, median, t-stat vs zero, and the Pearson correlation between event magnitude and reaction.
6. Detect regime stability per references/regime-stability.md: for any aggregate-mode result, compare the most-recent 4 events to the full window mean and flag when the gap is >1σ. Recent regime often differs from headline number.
7. Generate the take off the strongest signal: significant t-stat, regime shift, or surprise-vs-reaction correlation in cross-section.

Foundations used

• massive-api-patterns for REST auth, pagination, and the fallback chain.

Endpoints used

Earnings event class:

• GET /benzinga/v1/earnings?ticker={T}&limit=20&order=desc&sort=date (Tier A): press release date, time, surprise %, fiscal period.
• GET https://data.sec.gov/submissions/CIK{cik}.json (Tier B fallback): SEC EDGAR 8-K filings filtered to item 2.02. Free, public, no API key required. Same date-resolution logic as earnings-drilldown Tier B.

Dividend change event class:

• GET /v3/reference/dividends?ticker={T}&limit=20&order=desc&sort=ex_dividend_date: cash dividend history; the resolver picks the first ex-date where the cash amount differs from the prior payment by ≥1%.

Volume spike event class:

• GET /v2/aggs/ticker/{T}/range/1/day/{from}/{to}: same daily aggregates used for the return computation; the resolver computes volume z-score in-memory.

All classes:

• GET /v2/aggs/ticker/{T}/range/1/day/{from}/{to}: daily closes for the ticker. One call per ticker.
• GET /v2/aggs/ticker/SPY/range/1/day/{from}/{to}: daily closes for SPY (the benchmark). One call total.

Doesn't handle (yet)

• CAPM-style abnormal returns. The skill uses a simple SPY-naive benchmark (AR = raw − SPY). A v2 would estimate per-name beta on the 60-day pre-event window and compute AR = raw − (alpha + beta * SPY). For mega-caps the difference is small (beta is close to 1); for higher-beta names it matters. Schema reserves model: "spy" | "capm" so the upgrade doesn't break consumers.
• Multi-day pre-event run-up. Some event types (M&A leaks, guidance pre-announces) show abnormal returns before the official event date. The skill measures from T0 forward only.
• Sample-aware significance. Below n=8, t-stats are reported but marked "underpowered" rather than computing a small-sample correction. Bootstrap CIs would be cleaner; queued.
• Event clustering. When multiple events fire in the same window (e.g. earnings + dividend hike same week), the skill attributes the full return to whichever event the user asked about. A cleaner treatment would attribute by cross-section dummy; queued.
• Intraday windows. Event windows are daily closes only. No pre-market or 30-minute reaction measurement.

These are clean PR extensions and welcome contributions.