Stakeholder Identification

identify_stakeholders answers “who matters for this situation, and why?” It retrieves finance-specific evidence, returns structured public-company impacts with cited quotes, verifies tickers, and ships a factor comparison matrix.

Latency: commonly ~90 seconds; often 1–2+ minutes. Budget for that. Do not treat timeouts as “empty universe.” Do not spam parallel calls.

When to use

Use when you need:

• Ranked public-company winners/losers for a market situation, with direction + magnitude
• Why each name is exposed (rationale) plus auditable quotes
• A factor matrix to compare the returned names at a glance
• Backtest-safe identification as-of a historical end_timestamp

Do not use when you need:

• Fast headline triage → Wire
• Quoteable document chunks → semantic_search
• One company’s live dossier → retrieve_entity
• Already-stored development packages → search_developments (SI creates fresh assessments; developments store prior ones)
• Non-company stakeholders (countries, commodities, people, private cos) → out of scope (PUBLIC_COMPANY only)

Generic LLMs invent tickers; this endpoint retrieves evidence, cites exact quotes, verifies symbols, and returns a reusable factor table — with optional point-in-time mode.

Use cases

Realtime supply shock — Map who wins and loses if a concrete market shock hits, with verified tickers and cited reasons.

Code
{
  "query": "Strait of Hormuz near-total closure and oil supply shock"
}

Realtime with steering — Same exposure map, but steer coverage toward second-order names and keep the list tight.

Code
{
  "query": "Tesla Q2 2026 earnings miss and margin compression",
  "context": "EPS well below consensus; operating margin ~1.4%; negative FCF; energy storage strong.",
  "instructions": "Prioritize clear second-order exposures (EV peers, suppliers, AI compute). Prefer fewer high-confidence names."
}

Historical / backtest — Ask who mattered as of a past date, without live-web leakage after that cutoff.

Code
{
  "query": "Apple CEO succession Tim Cook to John Ternus",
  "end_timestamp": "2026-04-25T00:00:00Z"
}

Constrained universe — Force a short, sector-balanced list instead of a long undifferentiated dump.

Code
{
  "query": "who benefits from rising oil prices",
  "instructions": "Return at most 6 public companies spanning upstream, midstream, and airlines as losers."
}

Regulation / tariffs — Identify listed companies with clear regulatory exposure for a policy action.

Code
{
  "query": "EU Digital Markets Act fines on Big Tech self-preferencing",
  "instructions": "Focus on listed platforms and ad-tech peers with clear regulatory exposure."
}

Params

Prefer a concrete situation over a vague theme. Put hard facts in context; put process prefs in instructions ("Prefer fewer high-confidence names", "At most 6 names"). Instructions work — they change count and composition.

Only verified public tickers are returned as stakeholders.

Realtime vs backfill

Use backfill for “what would an analyst have concluded knowing only info ≤ T?” Use realtime for “given the live tape, who is in the blast radius?”

Returned tickers are verified; unverified names are dropped.

What you get back

stakeholder_impacts[]

• stakeholder: { name, stakeholder_type: "PUBLIC_COMPANY", symbol }
• impact_score ∈ [-1, 1]
• confidence_score ∈ [0, 1]
• rationale
• sources_with_quotes[] — exact quotes with source metadata

Scan strongest positives and strongest negatives first.

factor_matrix[]

Rows = situation-specific factors. Each has name (snake_case), description, factor_scale, rubric, optional allowed_values, and stakeholder_assessments keyed by symbol.

Scales: BINARY | CATEGORICAL | CONTINUOUS [0,1] | SIGNED_CONTINUOUS [-1,1].

Use the matrix for “who is more exposed on dimension X?” Factor names are meant to be reusable across similar situations. A factor row may occasionally omit a symbol — treat missing cells as unknown, not zero.

Workflow

1. Confirm the user wants situation → public-company exposure.
2. Craft a specific query; add context / instructions only when they sharpen the job.
3. Choose realtime vs backfill. Warn about ~1–2 min wait.
4. Call once; wait. On timeout, retry once — do not fan out parallel SI calls.
5. Brief: top ± impacts → factor matrix highlights → cite 1–2 quotes per key name.
6. Offer follow-ups via Wire / developments / retrieve_entity / semantic search using returned symbols.

Limitations

• Slow — plan for ~90s; avoid speculative retries and fan-out.
• Public companies only; listing suffixes vary (SMSN.IL, BYDDF, AMKBY).
• Verification can drop proposed names — final list ⊆ verified survivors.
• Not a price target or trading signal — impact scores are reasoned exposures with citations.
• Backfill quality depends on indexed corpus ≤ T.
• Factor matrix is built per call — read names/rubrics; don’t assume identical every time.
• Does not return countries/commodities as stakeholders even when they dominate the narrative.

FAQ

Q: Why is this better than asking a normal agent?
A: Retrieval + quote binding + ticker verification + factor matrix + backtest mode.

Q: Future end_timestamp — historical or live?
A: Treated as realtime.

Q: Can I get private cos / OPEC / Brent as stakeholders?
A: Not as structured stakeholders. They may appear in rationales/sources only.

Q: How do I continue after SI?
A: Take symbols → Wire filters, search_developments(stakeholder_symbol=…), retrieve_entity, or semantic search for passages.

Q: Empty / whitespace query?
A: 422 query must be a non-empty string.