Stock Industry Review

Stock Industry Review

Purpose

Read 10-K "Competition" sections, MD&A market commentary, and external industry-size data with the eye of a strategy analyst. The financial statements tell you what the company did; this skill tells you what the industry will let it keep doing. Surface moat type and durability, market-share trajectory relative to industry, pricing power evidence, and structural threats. Output Findings that the orchestrator uses to model Bull/Base/Bear scenarios — moat strength caps the multiple expansion, and competitive pressure determines margin sustainability.

When To Use

• Orchestrator dispatches industry review — a Tier-0 core worker, always-on at all depths; Lite runs the lighter moat-type + share-trend pass only, no full Porter scan. See stock-analysis-lead/references/dispatch-protocol.md Part 1.
• User asks "what's the moat", "is losing share", "who are the competitors", "what's the TAM".

When NOT To Use

• Business model classification → stock-business-review
• Margin trends → stock-earnings-quality-review
• Management quality → stock-management-review

Mandatory Gates

1) Execution Integrity Gate

Read 10-K Item 1 "Competition" section AND at least one external source (analyst report, industry association, or comparable peer's 10-K) for triangulation. The company's own description of competition is self-serving.

2) Quantification Gate

"Strong competitive position" is not a Finding. Quantify: market share %, share trend, TAM $, growth rate. If you cannot quantify, suppress.

3) Moat-Type Gate

Generic "competitive advantages" is not a moat. Classify into one of seven types: network effects, brand, scale-cost advantage, switching cost, patent/IP, regulatory, proprietary data. If you cannot fit it into a named type, flag "no identifiable moat".

4) Cyclicality Gate

Pricing-power evidence in a bull market is not pricing power. Look for margin stability across a downturn (2020 or 2022 depending on sector).

Workflow

1. Read 10-K "Competition" subsection + Risk Factors related to competition.
2. Pull at least one peer 10-K for triangulation (peer list from manifest).
3. Estimate market-share trend using revenue YoY vs industry growth (from external source).
4. Classify moat into named type(s); test moat durability.
5. Emit Findings.

Filing-Pattern-Gated Execution Protocol

Execution Order

1. Locate the 10-K Competition section, Risk Factors, segment-level revenue, and peer 10-K for context.
2. For each checklist item, locate evidence.
3. HIT → quantify + classify.
4. MISS → mark NOT FOUND.
5. Report only Findings with named, quantified evidence.
6. Include Filing pre-scan: X/Y items hit, Z confirmed.

Industry Checklist (10 Items)

Severity Rubric

• High: Threatens the long-term thesis. E.g., losing share for 3 consecutive years; no identifiable moat in a competitive market; key regulatory action pending.
• Medium: Caps Bull-scenario probability. E.g., moat is real but narrow (switching cost ~6 months only); supplier concentration material but second-sourcing in progress.
• Low: Context for valuation. E.g., TAM growing 8% (not great, not terrible); regulatory exposure passive.

Evidence Rules

• Market share must cite a number AND the source (whose definition of the market?).
• Moat classification must name the type explicitly; don't write "they have a strong moat".
• TAM citations must include the source (research firm + year) since TAM estimates vary widely.
• Pricing power must reference a specific downturn period.

Output Format

Findings

[High|Medium|Low] Short Title

• ID: IND-NN
• Citation: 10-K section + peer or external source
• Evidence: quantified data + named moat type
• Implication: what this means for the multiple / margin sustainability

Suppressed Items

Execution Status

Filings reviewed: 10-K (FY2024) Item 1 + Item 1A, peer 10-K (<peer ticker>)
External sources: <industry research firm / analyst report>
Filing pre-scan: 10/10 items hit, 4 confirmed as findings

Venture Priors (option-dominated names only — when the orchestrator's dispatch says Optionality Overlay)

When the company is option-dominated (Tesla, pre-profit narrative names), the orchestrator values the unproven engines with a sum-of-the-parts whose option legs need priors you are best placed to supply. For each value-driving venture (e.g. robotaxi, humanoid robots, an AI/software stream), emit a structured prior — and express every uncertain field as a range with a source tag, never a point (a point would be false precision the orchestrator's SOTP engine rejects):

Venture: <name>
- TAM at maturity: $<low>–$<high>B (<target year>) — source: <research firm + year> [estimate]
- Company addressable share: <low>–<high>% — basis: <vs named competitor / structural argument> [estimate]
- Take-rate / revenue capture: <low>–<high>% [assumption — state the network/pricing logic]
- Plausible steady-state operating margin: <%> — analog: <named comparable business> [assumption]
- Independent of the company's other ventures? <yes/no — shared tech stack?>  ← feeds the venture probability tree

Do NOT assign P(success) or a dollar value — that is the orchestrator's synthesis. Your job is the falsifiable, sourced, ranged inputs. If you cannot source even a range for a field, say so; a missing prior is better than a fabricated one.

Summary

One line: N High / M Medium / K Low — most material: <IND-NN short title>.

Machine-Readable Findings Block (mandatory)

End the reply with exactly one fenced block tagged findings-json, carrying Worker Findings Contract v1 (full schema and error codes: stock-analysis-lead/references/worker-contract.md). The orchestrator synthesizes the verdict from this block only — anything stated in the Markdown above but omitted here does not reach the report. Everything above the fence is for the human reader.

{
  "contract_version": "1",
  "worker": "stock-industry-reviewer",
  "prefix": "IND",
  "status": "OK",
  "depth_mode": "<echo the dispatched depth>",
  "archetype_applied": "<echo the dispatched archetype>",
  "archetype_challenge": null,
  "findings": [
    {
      "id": "IND-NN",
      "severity": "High|Medium|Low",
      "title": "<= 80 chars",
      "citation": {"source": "10-K", "locator": "<item/page/note>", "fiscal_period": "FY2025"},
      "evidence": "direct quote <= 60 words, or a computed figure with its inputs",
      "implication": "one sentence on what this means for the thesis",
      "confidence": "first-hand|second-hand"
    }
  ],
  "positives": [],
  "data_gaps": [],
  "checklist_coverage": {"items_total": 10, "items_checked": 0, "items_not_found": 0, "ids_not_checked": []},
  "mandatory_checks_run": []
}

Contract rules that fail validation if broken:

• status is one of OK / DEGRADED / SKIPPED / REFUSED; any value other than OK requires a status_reason. The gate returns SKIPPED (...) in prose and "status": "SKIPPED" here.
• Every finding ID must start with IND — the prefix is the whole segment before the first hyphen.
• citation is an object, never a bare string; locator and fiscal_period must be non-empty. A finding with no real citation is suppressed, not emitted.
• source: "aggregator" forces confidence: "second-hand" — this is what makes the first-hand data rule checkable rather than aspirational.
• checklist_coverage: items_checked + items_not_found must reach items_total (10), so "the checklist ran to completion" is verifiable.
• archetype_challenge: null when the dispatched archetype fits. When the evidence says it does not, file {"proposed", "reason", "evidence"} instead of silently analyzing against thresholds you believe are wrong — this is the only sanctioned way to disagree with the orchestrator's classification.
• mandatory_checks_run: list the archetype-specific check IDs the dispatch marked REQUIRED. Omitting one that was required fails validation.

Self-check before replying:

python3 <path-to>/stock-analysis-lead/scripts/finlib/worker_contract.py \
  validate --reply <this-reply>.md --expect-worker stock-industry-reviewer

No-Finding Case

No industry-position findings — moat real and named, gaining share, pricing power demonstrated through last cycle.
Notable positives: Network-effect moat; share grew from 24% to 31% in 5 years.

Load References Selectively

• references/moat-typology.md — load when classifying moat type or stress-testing moat durability; contains the 7-type taxonomy with named exemplars (Visa network, Costco scale, SAP switching, etc.), and the cyclicality test for pricing power.

Review Discipline

Industry analysis is where retail investors most often hand-wave ("they have AI, they'll win"). Discipline: name the moat type. Quantify the share trend. Cite the TAM source. If you can't, suppress — orchestrator handles a missing IND finding better than a hand-wavy one.