Equity-sleeve coverage88.3%88.2% covered of 99.9% retained equity weight.
Total-fund coverage88.2%Company fundamentals as a share of 100.0% reported fund weight; no reallocation.
Non-company exposure0.1%Cash, derivatives, and other non-equity rows are excluded · tradingview_etf_holdings · Jul 20, 2026 · current.
Partial mapped company sleeve54/54 mapped companies have stored fundamentals, covering 88.3% of the retained company sleeve and 88.2% of fund weight. The remaining 0.1% is non-company exposure, not missing companies. Separately, 6 equity holdings (11.7% of fund weight) remain unmapped.
Return factors and risk factors built from stored company fundamentals, price tape, themes, event evidence, liquidity, and flow datasets.
Companies54Ranked inside this universe
Factors5034 ready · 16 partial
Coverage98%Average ready-factor coverage
As ofJul 21, 9:30 PM GMT+8Latest stored factor input
Full factor lab detail queuedThe initial factor map is loaded from the compact snapshot; full histories, heatmaps, validation matrices, and lenses hydrate on demand.
Factor workflow
Navigate the factor engine without scrolling the whole report
Start with attribution quality, move into the action book, then inspect individual factor evidence only when needed.
Daily stock-level cross-sectional regressions estimate factor returns from company returns and reusable economic factor exposures. Sector, industry, theme, and country are reporting lenses, not dummy factors used to force-fit the universe return.
For each stored trading day, company returns are regressed against reusable company-level factor exposures. The common market column captures the broad universe tape; style factors explain tilts around that tape.
Lens policyNo fake fit from buckets
Sector, industry, country, and theme are reporting lenses only; they are not dummy variables used to force-fit returns.
Attribution splitWhat is investable versus only explanatory
24 usable factors · 23 diagnostic factors
Common tape-4.11%Broad universe move before active factor interpretation.Rewarded / usable factors-1.05%19 rewarded · 5 risk-controlDiagnostic factors+1.55%21 diagnostic · 2 blockedResidual-0.29%Idiosyncratic return that should flow into company-level research.
Attribution policy. Separate common tape, investable rewarded factors, diagnostic factors, and residual. Diagnostic factors explain movement but are not portfolio-sizing inputs.
Factor action bookWhat to overweight, hedge, block, or research next
Overweight · -4.68% actionable return
Active tilts19Risk controls5Blocked2Research-only21Regime-adjusted19
Small / validation pendingConviction 59 · risk budget n/a%
Contribution
+0.16%
Factor return
+0.81%
Exposure
+0.20
Capital discipline quality is a rewarded factor with positive contribution and usable breadth.
Regime-aware sizingSmall / validation pending
No stored regime evidence · best n/a · worst n/a · n/a% survival
No stored regime validation yet; keep sizing small until regime behavior is refreshed.
Coverage passValidation watchPayoff failCrowding failRisk budget watchRegime fit watch
Use the action book as a review order. Active tilts require validation, breadth, uniqueness, controlled crowding, and regime survival; diagnostic rows remain research-only.
Model fit disciplineWhen factor attribution is reliable enough to use
Usable factor fit
Current stock-level fit58%Weighted R-squared across latest company returns.Average daily fit57%How often the model explains day-to-day cross-sectional moves.Fit stability58%Share of stored sessions with usable stock-level factor fit.Residual cost+4.36%Average stock-level error; gross residual +3.40%.
Not an accounting identity. R-squared is a stock-level fit diagnostic, not a promise that factors explain every portfolio return dollar. The residual board remains the source of company-specific work.
Use factors as the first pass, then verify residual groups and company-specific outliers before sizing.
Factor trust dashboardCan this attribution guide positioning?
Usable with checks
Trust score65
Use smaller factor tilts; validate residual clusters and crowded factors before sizing.
Positioning guidanceUsable with checks
Use smaller factor tilts; validate residual clusters and crowded factors before sizing.
Weakest fit dayJun 29, 2026
30% fit · RMSE +1.68%
Residual-dominated days15
Latest fit 98% · average 57%
Required checks before sizing2 passed
Stock-level fitwatch57 · Stock-level fit is usable but should be checked against residual outliers.
Residual loadpass16 · Residual sleeve is controlled enough for a factor-first review.
Validation depthwatchn/a · Run forward validation before treating factor payoffs as durable.
Crowding controlfail100 · Top factors or redundant factors dominate; cap position size and avoid double counting.
Diagnostic factor loadpass25 · Most model risk comes from usable rewarded or risk-control factors.
Failure modes5 checks
Weak stock-level fitmediumThe cross-sectional model is not consistently explaining stock returns.
Weakest fit dayhigh2026-06-29 had only 30.0% stock-level explanatory fit.
Validation depthmediumForward validation is not strong enough to treat every factor payoff as durable.
Crowding controlhighA small number of sleeves or overlapping factors can dominate the attribution.
Residual-dominated daysmedium15 stored sessions had more residual than modeled factor fit.
Factor reliability boardWhich factors are investable, confirming, or noisy
55 average reliability · 0 decision-grade factors
Decision-grade tilts0Validated enough for active factor interpretation
No factor clears all reliability gates yet.
Usable confirmation4Useful when independent evidence agrees
Growth accelerationUse as a confirming factor alongside other independent evidence.
Reliability
77
Validation
n/a
Crowding
29
Earnings revision pressureUse as a confirming factor alongside other independent evidence.
Reliability
67
Validation
n/a
Crowding
44
BetaUse as a confirming factor alongside other independent evidence.
Reliability
63
Validation
n/a
Crowding
70
QualityUse as a confirming factor alongside other independent evidence.
Reliability
62
Validation
n/a
Crowding
62
Noisy or incomplete8Needs better validation, coverage, or stability
ValueUse as a descriptive exposure until forward tests are populated.
Reliability
56
Validation
n/a
Crowding
69
LeverageUse as a descriptive exposure until forward tests are populated.
Reliability
53
Validation
n/a
Crowding
62
LiquidityUse as a descriptive exposure until forward tests are populated.
Reliability
53
Validation
n/a
Crowding
77
Drawdown repairUse as a descriptive exposure until forward tests are populated.
Institutional flowUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
63
Validation
n/a
Crowding
99
Fund-flow confirmationUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
62
Validation
n/a
Crowding
99
Revenue acceleration leadershipUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
64
Validation
n/a
Crowding
99
Flow-momentum confluenceUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
57
Validation
n/a
Crowding
99
No factor currently clears the decision-grade validation, stability, and uniqueness gates.Some factors are highly correlated with peers, so do not add their attributions together mechanically.Some factors are descriptive only until forward validation or coverage improves.
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