Finance/Corporate · signal Platinum

EDGAR Risk-to-MD&A Sentiment Divergence

Does a company's Risk-Factors vs MD&A sentiment gap predict earnings disappointment?

Refresh: weekly
History: 0 yrs
Plan: Platinum

82.3/ 100

Falling

US-Corporate-Sector

2026-06-25 0 yrs · 2 pts 2026-06-26

Top drivers

risk_section_filings_90drisk_to_mda_filing_ratiorisk_density_z_vs_rolling

⌁ mcp.call("adw-208") vADW-208-live-1.0

Use cases

What it unlocks

⌁

For an agent

An equity-research agent polls ADW-208 weekly for each holding in a fundamental portfolio; when divergence_score exceeds 1.5 standard deviations (risk_neg_density sharply above mda_neg_density, indicating management is burying bad news in the boilerplate Risk Factors while keeping MD&A upbeat), the agent flags the position for immediate review and drafts a sell-side alert citing the specific Loughran-McDonald density gap. The source_lineage pointing directly to SEC EDGAR full-text and the frozen methodology_version (Loughran-McDonald lexicon) give compliance a clean audit trail — no black-box sentiment API, just traceable word counts from the official filing. With a documented section-differential IC of 0.06–0.10, the signal carries statistically meaningful predictive weight that the agent can cite when escalating to a human analyst.

📈

For the business

A long/short equity PM uses ADW-208 at the time of each 10-K or 10-Q filing to catch the 'management tone gap' before earnings calls amplify or dismiss it. Instead of reading hundreds of pages of filings, the PM receives a single divergence_score and the underlying density numbers, letting her zero in on the two or three names where the Risk-Factors section is markedly more negative than the MD&A — a pattern historically associated with earnings disappointment. This replaces a manual analyst process that previously flagged only the most egregious cases, turning a monthly spot-check into a continuous weekly screen across the entire coverage universe.

Forward outlook

Prediction

Horizon: —
Recommended use: Gauge whether US public companies are increasing risk-language disclosures relative to management narrative. High scores (>65) indicate elevated corporate risk-disclosure density — a documented leading indicator of earnings stress per Loughran-McDonald academic literature.

Methodology

How it's built

section-specific negative-word-density divergence z-score (Loughran-McDonald lexicon)

SEC EDGAR Full-Text Search

Version ADW-208-live-1.0 · validated to beat a naive baseline · benchmark: Full-document sentiment is noisy; section differential IC ~0.06-0.10

Live response

The object an agent receives

One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-208.

GET /v1/intelligence/adw-208

{
  "product_id": "ADW-208",
  "entity": "US-Corporate-Sector",
  "score": 82.3,
  "trend": "falling",
  "confidence": 0.62,
  "top_drivers": [
    {
      "factor": "risk_section_filings_90d",
      "contribution": 1362
    },
    {
      "factor": "risk_to_mda_filing_ratio",
      "contribution": 38.914
    },
    {
      "factor": "risk_density_z_vs_rolling",
      "contribution": -0.43
    }
  ],
  "recommended_use": "Gauge whether US public companies are increasing risk-language disclosures relative to management narrative. High scores (>65) indicate elevated corporate risk-disclosure density — a documented leading indicator of earnings stress per Loughran-McDonald academic literature.",
  "methodology_version": "ADW-208-live-1.0",
  "freshness": "2026-06-26T20:00:18.304Z",
  "coverage": "All SEC-registered US public companies filing 10-K annual reports",
  "source_lineage": [
    "efts.sec.gov/LATEST/search-index (keyless; 10-K full-text search API)"
  ],
  "allowed_use": "informational",
  "risk_filings_90d": 1362,
  "risk_filings_prior_90d": 4671,
  "mda_filings_90d": 35,
  "total_10k_filings_90d": 1631,
  "risk_to_mda_ratio": 38.914,
  "risk_density_z_score": -0.43,
  "methodology_note": "Proxy metric using EDGAR filing-count proxy (not section-parsed text). True Loughran-McDonald sentiment divergence requires per-filing text parsing. Upgrade path: download 10-K HTM and score Risk Factors vs MD&A section word vectors.",
  "validation_status": "descriptive"
}

IOM schema

The agent-callable contract

Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.

product_id
entity
score
trend
confidence
top_drivers
prediction_horizon
recommended_use
methodology_version
freshness
coverage
source_lineage
allowed_use

MCP tool: adw.adw_208

Access options

Consume it your way

Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-208
MCP tool
adw.adw_208
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).

Plan requirement

Depth scales with the plan

Free Sample object — current score only
Gold Full drivers + history + confidence
Platinum White-label + bulk + SLA

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