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Next filing · Form 10-Q · Q2 2026 · 54 daysFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 3 · The One Manager Skill That PersistsPart 1 · One Position, Four BetsNext filing · Form 10-Q · Q2 2026 · 54 daysFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 3 · The One Manager Skill That PersistsPart 1 · One Position, Four Bets
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Concept · The RiskModels engine (ERM3)

Hierarchical cascade (L1/L2/L3)

L1 = market (SPY); L2 = sector ETF (e.g. XLK) fit on the L1 residual; L3 = subsector ETF (e.g. SOXX) fit on the L2 residual. Each level explains what the prior one left over.

In depth

L1 fits market (SPY); L2 fits the sector ETF on what L1 left over; L3 fits the subsector ETF on what L2 left over. Each level explains only its incremental risk, so the shares are additive.

Formula

r = β_m·r_m (L1) → β_s·r*_sector (L2) → β_u·r*_sub (L3) → ε

Compute it with the API

POST /api/decompose

# pip install riskmodels-py
client.decompose("NVDA")

Full API docs ↗

In the methodology

The three levels: L1, L2, L3 →

Referenced by (2)

  • Cascade Hedging and the Cost of Interpretability

    Subsector ETF value, joint optimization, and executable hedge layers across 9,074 US mutual funds

  • ERM3 Cascade-Residual Persistence and the Allocator Skill Signal

    Top-decile rank persistence, active-share comparison, and tail-stratified inference across 1,000 top-AUM US mutual funds

Related concepts

ERM3Link beta (λ)Explained risk (ER)Replication equationRobust beta (Huber-M)Vasicek shrinkageL-star ruleGeometric attribution bridgeResidual mean-reversion signal
← ERM3The RiskModels engine (ERM3) · 2 / 10Link beta (λ) →
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