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Next filing · Form 10-Q · Q3 2026 · 27 days
Next filing · Form 10-Q · Q3 2026 · 27 daysAPI Update · Point-in-Time (PIT) historical commit tracking — securities and funds unifiedAPI Update · ERM3 L3 variance partition — institutional transparency releaseFactor Research · L-Star adaptive hedge depth — out-of-sample validation across ten allocator universesPart 1 · One Position, Four BetsPart 2 · Risk Structure in 13F FilingsNext filing · Form 10-Q · Q3 2026 · 27 daysAPI Update · Point-in-Time (PIT) historical commit tracking — securities and funds unifiedAPI Update · ERM3 L3 variance partition — institutional transparency releaseFactor Research · L-Star adaptive hedge depth — out-of-sample validation across ten allocator universesPart 1 · One Position, Four BetsPart 2 · Risk Structure in 13F Filings
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Legible risk

A risk map, not a factor zoo.

A factor zoo is a collection of labels. A risk map shows relationships. RiskModels organizes each position across market, sector, subsector, and residual layers, connects the systematic layers to liquid ETF references through model-derived hedge ratios, and leaves stock-specific risk visible.

The problem

Most portfolios assume benchmark risk instead of measuring it. Style labels blur what is broad-market, sector, subsector, and genuinely stock-specific.

The method is published. The output is legible. The judgment stays with the user.

Get a free API keyRun on your portfolio

New here? Start with the concept glossary →

Equity risk mapBenchmark → bet
  1. Market01

    Broad benchmark

  2. Sector02

    Sector exposure

  3. Subsector03

    Industry-level tilt

  4. Residual04

    Stock-specific risk

Four layers. Published method. One API call.

Live risk decomposition

Four bets: market · sector · subsector · residual.

Risk decomposition

MSFT

variance share by layer
  • Marketno ETF—
  • Sectorno ETF—
  • Subsectorno ETF—
  • Residualno ETF—

This is one /decompose call — an app or agent gets the same object.

The API

Read the residual bar first — it’s the share of risk that’s genuinely stock-specific, the part broad ETF hedges can’t replace.

σ_p² = β_p²σ_m² + σ_ε² — rentable benchmark risk plus owned residual.

For AI-native finance

An assistant shouldn’t invent portfolio-risk commentary from text memory — it should call a structured, time-stamped model it can cite. The same auditable decomposition is exposed as agent-ready objects: four risk layers, ETF hedge ratios, and interpretable context. See the agent interface →

What it measures · the four bets

Market
Broad SPY beta — the rentable core of the position.
Sector
GICS sector exposure incremental to the market.
Subsector
The granular industry tilt that style labels miss.
Residual
Idiosyncratic risk that can’t be replicated with broad ETF exposure.

Estimated bottom-up: market (L1) → sector (L2) → subsector (L3) → residual, each level orthogonal to the last. Full derivation →

Core concepts

The vocabulary, bottom-up — stock risk → portfolio aggregation → decomposition.

ResidualHedge ratioExplained risk (ER)Hierarchical cascade (L1/L2/L3)Active sharePoint-in-time (PIT)Return attributionERM3
Browse all concepts →

Selected research

  • Working paper

    NVIDIA: The Economic-Profit Burden of an AI Bottleneck

    A RiskModels.app visual case study in market-implied expectations

    Read →
  • Part 1

    One Position, Four Bets

    Turning conviction into tradeable risk: same label, different bets across AAPL/NVDA, XOM/KMI, and MAG7.

    Read →
  • Part 2

    Risk Structure in 13F Filings

    Market, thematic, and stock-specific risk across Buffett, Ackman, Lone Pine, Tiger Global, and Baupost — and what survives the 45-day filing lag.

    Read →
Full research library →

Out-of-sample: 5,910 stocks, 19 years

  • Replication identity verified to ±0.1% — every stock, every day
  • Explained-risk shares sum to 100% by construction
  • Liquid ETF hedge basket — SPY / XLK / SOXX, no synthetic factors
Full derivation & validation →

Built like infrastructure

  • Point-in-time returns — split- & dividend-adjusted, time-safe back to 2006 — ~20 years of daily factor history
  • Bi-temporal master — valid-from / valid-to windows mask every date by universe and validity — no recycled tickers or retroactive universe contraction
  • Headline universe — ~3,000 largest US names, dual-gated by a monthly universe mask and a daily price-validity gate
  • Orthogonal L3 cascade — market (SPY) · 11 GICS sector SPDRs · subsector ETFs, each a 252-day rolling Huber-M beta
  • ETF history stitched — proxy splices (VNQ→XLRE, VOX→XLC) keep long-horizon decompositions consistent — pre-inception returns stay NaN, never zero-filled
  • Dagster pipeline — daily end-of-day refresh (incremental weekdays, full rebuild month-end) — Zarr on GCS, snapshots in Supabase
How the engine is built →

What people are saying

  • “RiskModels has a credible claim as the quantitative truth layer behind financial agents.”

    — ChatGPT, May 2026

  • “In a world full of risk models that overpromise and under-explain, RiskModels is refreshingly honest, technically excellent, and immediately actionable.”

    — Grok, May 2026

Read the reviews →

The ecosystem

  • riskmodels.orgResearch · method, proof, exhibits
  • riskmodels.netWorkspace · preview · in development
  • riskmodels.appAPI · REST · SDK · MCP

Quarterly Attribution Review

Subscribe to the Quarterly Attribution Review.

Research notes on risk decomposition, fund attribution, 13F filings, and benchmark structure — a few times a quarter.

By registering, you agree to receive technical factor research and API deployment logs. RM-Registry-2026. Privacy Policy.

Exhibits · live benchmark artifacts

refreshed each 13F / N-PORT cycle · next ≈ Q2 2026

RiskModels.org

A research surface for hierarchical orthogonal decomposition, variance attribution, and allocator-grade risk measurement. Operational APIs and developer workflows live at riskmodels.app.

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