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Factor Model Methodology

This page is for allocators, portfolio managers, risk analysts, and compliance teams evaluating SEC API’s factor model for portfolio construction, attribution, and risk management.

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Overview

SEC API provides an allocator-grade factor research catalog spanning more than 230 tracked factor definitions across market, style, macro, sector, industry, country, and thematic categories. The launch API surface is narrower: U.S. market, style, sector, and industry factors target a 2010-01-01 history floor, while shorter-history, beta, blocked, or deferred factors are labeled in catalog and methodology metadata. Public 2010+ claims should be made only where row-level coverage and freshness proof support the factor. The model follows a hierarchical purification architecture consistent with institutional standards pioneered by MSCI Barra and adopted by Axioma (Qontigo), Bloomberg PORT, and Northfield. Launch factor return surfaces are refreshed on a market-calendar-aware schedule. Intraday snapshot endpoints expose available in-session factor state when the underlying market-data plane is current; inspect freshness metadata before treating a snapshot as live. For source posture, source-rights metadata, and response trust fields, see Factor Provenance. For market-calendar SLAs and freshness interpretation, see Factor Freshness.

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Factor categories

CategoryCountDescription
market1Broad US equity risk premium (MKT_US)
style30Cross-sectional return drivers (momentum, value, quality, size, low-volatility, etc.)
macro17Macroeconomic regime factors (rates, credit, commodities, currencies)
sector26GICS sector and sub-sector return attribution
industry31Industry-level return attribution
country45Single-country and regional equity factors
thematic81Named investment themes (nuclear energy, genomics, REITs, etc.)

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Factor construction pipeline

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Step 1: Universe construction

U.S. market, style, sector, and industry stock-basket factors are computed over a point-in-time U.S. equity universe refreshed monthly:
  • Minimum $100M market capitalization
  • Minimum $5M average daily dollar volume
  • Minimum $1.00 share price
  • Excludes warrants, units, rights, ETFs, ETNs, ADR certificates
  • Exchanges: NYSE, NASDAQ, AMEX, ARCA, BATS
Universe membership is determined as of each rebalance date using data available at that time — no look-ahead bias.

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Step 2: Constituent sourcing

Each factor type uses a different data source for membership and characteristic computation:
SourceFactor typesExample
Point-in-time fundamentals (SEC XBRL)Style factorsBook/Market ratio from latest 10-K filing
SIC classificationSector and industry factorsSIC 7372 → Technology / Software
ETF, proxy, and curated basket metadataThematic, macro, and country factorsURA proxy spread → Nuclear Energy basket
Curated constituent listsThematic baskets without ETF proxyHotel REITs: APLE, HLT, MAR, HST…
Single-country ETFCountry factorsEWJ → Japan equity premium

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Step 3: Return computation

Style factors: Long-short decile portfolio sorts. Stocks are ranked by a characteristic (e.g., book-to-market for Value), divided into decile portfolios, and the factor return is the spread between the top decile (long) and bottom decile (short). Portfolios are cap-weighted within each leg and rebalanced monthly. Thematic baskets (ETF-backed, 66 factors): Factor return is the ETF return minus a relevant benchmark. The ETF provider (Global X, ARK, VanEck, iShares, etc.) handles constituent selection and rebalancing. Example: THEMATIC_NUCLEAR_ENERGY = R(URA) - R(SPY). Thematic baskets (curated, 15 factors): Factor return is the equal-weight constituent basket return minus a relevant benchmark. Constituents are sourced from public factor composition data and reviewed quarterly. Example: THEMATIC_CASINO_LEISURE = (1/9) · Σ R(casino stocks) - R(SPY). Macro and country factors: ETF proxy spread versus a reference instrument. Example: RATES = R(TLT) - R(SHV).

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Step 4: Hierarchical purification (orthogonalization)

Each factor declares 1–3 parent factors and is purified against them using rolling 156-trading-day OLS regression to extract the residual return not explained by parent factors. 
Market (MKT_US)
├── Style factors (SIZE, VALUE, MOMENTUM, ...)
│   └── purified against: MKT_US
├── Sector factors (SECTOR_ENERGY, SECTOR_TECH, ...)
│   └── purified against: MKT_US
├── Thematic baskets
│   ├── Energy themes → purified against: MKT_US, SECTOR_ENERGY
│   ├── Tech themes → purified against: MKT_US, SECTOR_TECH
│   ├── REIT themes → purified against: MKT_US, THEMATIC_REITS
│   └── Country themes → purified against: MKT_US, regional parent
└── Country factors
    └── purified against: MKT_US, regional parent
Why hierarchical purification? The choice to purify against a small number of theoretically motivated parent factors — rather than all factors simultaneously — is a deliberate methodological decision supported by decades of factor research:
  • Parsimony: Fama & French (2018, Journal of Financial Economics) argue that factor models should include only factors that earn their place. Redundant factors should be excluded, not controlled for via regression.
  • Overfitting resistance: Harvey, Liu & Zhu (2016, Review of Financial Studies) show that including many correlated regressors inflates false discovery. Kozak, Nagel & Santosh (2020, Journal of Financial Economics) demonstrate that shrinkage estimators dominate unrestricted high-dimensional OLS.
  • Institutional alignment: MSCI Barra’s US Equity Model (USE4) uses hierarchical nesting: market → country → industry → style. Axioma and Bloomberg follow similar architectures. No major institutional risk model provider uses unrestricted regression against 100+ factors.
  • Signal preservation: Purifying a housing theme against market + real estate sector preserves the thematic signal. Over-purification against all known factors strips out the very characteristics that define the theme.

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Step 5: Dynamic volatility targeting

After purification, each factor’s residual return series is dynamically leveraged to target 10% annualized volatility:
  • Rolling realized volatility computed over the same 156-day window
  • Leverage = min(target_vol / realized_vol, 3.0)
  • Prevents any single factor from dominating portfolio-level attribution
  • Consistent with standard practice at AQR, MSCI, and major factor ETF providers

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Step 6: Z-score computation

Scaled returns are converted to rolling z-scores for cross-factor comparability. Z-scores are the primary output for dashboards, screening, and signal generation.

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Intraday factor snapshots

During US market hours (9:30-16:00 ET), SEC API exposes intraday factor snapshots when current proxy and constituent inputs are available. Each snapshot can include raw return, purified return, scaled return, z-score, and freshness metadata so consumers can distinguish current, stale, or degraded results.

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Data provenance and traceability

Every factor return observation includes:
  • requestId and traceparent for end-to-end request tracing
  • modelName identifying the computation pipeline version
  • methodology.inputs listing the data sources consumed
  • sourceRights.notes documenting source-rights status when trust metadata is included
Compact responses may omit heavier trust metadata unless requested. Use response_mode=compact&include=trust on REST endpoints or include: ["trust"] in MCP tool calls when an agent needs freshness, materialization, methodology, source-rights, and degraded-state metadata alongside the answer.

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Methodology comparison

FeatureSEC APIMSCI Barra (USE4)
PurificationHierarchical, 1–3 declared parentsHierarchical nesting
Estimation windowRolling 156-dayRolling 252-day with exponential decay
Factor hierarchyMarket → Sector → ThemeMarket → Country → Industry → Style
WeightingCap-weight (style), equal-weight (thematic)Cap-weight
Volatility targeting10% annual, dynamic leverageFactor-specific
RecomputationMarket-calendar-aware daily refresh plus intraday snapshots when current inputs are availableDaily
Factor count230+ tracked definitions, narrower launch API surfaceVaries by model

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Thematic basket methodology

SEC API’s 81 thematic baskets cover named investment themes across energy, technology, healthcare, financials, real estate, consumer, transport, ESG, and regional equity. Two construction modes:
  1. ETF-backed (66 baskets): An institutional ETF provider handles constituent selection and rebalancing. SEC API computes the spread versus a relevant benchmark and applies hierarchical purification. Examples: genomics (ARKG vs XLV), nuclear energy (URA vs SPY), cybersecurity (CIBR vs XLK).
  2. Curated constituent (15 baskets): Equal-weight portfolio of named constituents versus a benchmark. Constituents reviewed quarterly. Examples: hotel REITs (18 stocks vs VNQ), trucking (16 stocks vs XLI), alternative asset managers (10 stocks vs XLF).
Both modes pass through the same hierarchical purification and volatility-targeting pipeline.

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Academic references

  • Ang, A., & Kristensen, D. (2012). Testing conditional factor models. Journal of Financial Economics, 106(1), 132–156.
  • Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. Journal of Finance, 68(3), 929–985.
  • DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification. Review of Financial Studies, 22(5), 1915–1953.
  • Fama, E. F., & French, K. R. (2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252.
  • Harvey, C. R., Liu, Y., & Zhu, H. (2016). …and the cross-section of expected returns. Review of Financial Studies, 29(1), 5–68.
  • Kozak, S., Nagel, S., & Santosh, S. (2020). Shrinking the cross-section. Journal of Financial Economics, 135(2), 271–292.
  • Menchero, J., Orr, D. J., & Wang, J. (2011). The Barra US equity model (USE4). MSCI Barra Research.
  • Patton, A. J., & Verardo, M. (2012). Does beta move with news? Review of Financial Studies, 25(9), 2789–2839.