How to Study Material Cyber-Incident Stock Reactions

Study material cyber-incident stock reactions from the first Item 1.05 filing timestamp, stratified by disclosed operational disruption and restoration uncertainty before estimating abnormal returns.

A material cyber-incident stock-reaction study should define severity from facts the issuer disclosed at the event time, not from later headlines. Start with original Form 8-K Item 1.05 filings, map each EDGAR acceptance timestamp to a trading session, and create cohorts based on stated operational disruption, data compromise, third-party origin, and restoration status. Alphanume's Cyber Incidents dataset preserves those facts as three-state fields and separates original disclosures from amendments.

The Cyber Incidents documentation describes the filing timestamps, four date roles, flags, source links, and amendment behavior. The dataset reports disclosed facts and conservative classifications. It does not reveal undisclosed severity, prove that the incident caused a return, or estimate expected performance.

Anchor the event to public disclosure

Use filing_timestamp as the public event clock. The incident discovery date and materiality determination date describe internal process milestones when the filing states them. They are not automatically public on those dates. A filing accepted after the close should map to the next eligible session under a prespecified rule. Retain the timestamp and offset so another researcher can reproduce day zero.

Each extracted incident and materiality date has a precision field. Month-precision dates are normalized to the first day of the month, so they cannot support day-level latency. Use precision filters when timing intervals matter. The served interval from materiality determination to disclosure is in calendar days, while the SEC deadline is expressed in business days.

Build the original-disclosure panel

Filter is_amendment=0 and retain CIK even when ticker is missing. The Item 1.05 corpus begins on 2023-12-18 and is small because material disclosures are rare. That is the defined population, not a reason to fill the sample with rumors or cyber stories disclosed under unrelated channels.

GET /v1/regulatory/cyber-incidents
is_amendment=0
date_gte=2024-01-01
date_lte=2025-12-31

Keep: accession_number, cik, ticker, filing_timestamp, attack_type,
operations_disrupted_flag, systems_restored_flag,
data_compromised_flag, third_party_incident_flag,
investigation_ongoing_flag, market_cap_at_filing, filing_url

day_zero = session_map(filing_timestamp, close_rule)

Use accession number as the unique event key. Poll updated_since when maintaining a current research table because later amendment linkage can update an original row. For strict point-in-time reproduction, version raw responses and exclude facts first disclosed in later amendments from the original event feature set.

Define disruption cohorts without collapsing unknowns

The core comparison can separate stated disruption from stated no disruption and unstated. Within each group, restoration status can distinguish stated restored, stated not restored, and unstated. Keep these as categories rather than converting null to zero. Silence may reflect disclosure style, incomplete investigation, or genuinely unavailable information. The dataset does not identify which explanation applies.

• Stratify on facts present in the original filing only.
• Report three-state flag counts before returns.
• Use attack type as a disclosed taxonomy, not a monetary severity estimate.
• Join point-in-time sector and market cap when constructing matched controls.

A matched-control design can pair events with firms of similar sector and pre-event size, then estimate returns over fixed windows. A market-model design is another option if the estimation window has enough clean price history. In either case, publish the benchmark, estimation period, event windows, weighting, and treatment of overlapping market shocks.

Predefine amendments and confounders

The main failure mode is allowing later amendments to redefine the original cohort. An amendment may clarify scope, restoration, or data compromise, but that fact was not necessarily known on day zero. Use later disclosures for a separate information-update study. Do not overwrite the original feature vector and then claim the market initially reacted to those later details.

Also flag earnings, guidance changes, financing, litigation, and other material news released in the same filing or window. Cyber disclosure does not establish that the cyber incident alone caused the observed return. Show results with and without confounded events. Securities with missing prices, halts, ticker changes, or delistings should remain in an outcome-status table rather than disappearing from the denominator.

Sample size is another hard limit. Item 1.05 has existed only since late 2023, and the full population of material filings remains small. Avoid splitting the data into so many attack and disruption cells that a single issuer determines the estimate. Report event-level observations, medians, and uncertainty alongside averages, and frame the result as evidence from the available disclosure regime rather than a timeless cyber-risk premium.

Run a small, auditable reaction study

As a concrete next action, retrieve a completed period of original disclosures and build a review sheet with timestamp, mapped session, three-state flags, market cap, filing URL, and confounder notes. Audit each filing in a small pilot. Then join sector and point-in-time market cap for controls, attach returns, and calculate prespecified windows for the stated-disruption, stated-no-disruption, and unstated cohorts. Publish event counts and missing outcomes beside the return estimates. That design tests a narrow, reproducible question without converting sparse disclosure facts into an invented cyber-severity score.