Building Custom Factor Definitions

Building Custom Factor Definitions

When To Use

• Defining a new proprietary signal (e.g., custom momentum, quality, or alternative-data factor) for systematic portfolio construction
• Extending an existing factor library with additional alpha or risk factors
• Formalizing an ad-hoc trading signal into a documented, production-ready factor definition
• Orthogonalizing a raw signal against standard risk factors (market, size, value, momentum) to isolate residual alpha
• Specifying universe filters, rebalance cadence, and neutralization rules for a new factor

Inputs To Gather

• Signal hypothesis: Economic rationale for why the signal should predict returns (e.g., earnings revision momentum captures analyst under-reaction)
• Raw data source(s): Vendor, frequency, history depth, point-in-time availability, and known gaps [VERIFY data lag and survivorship-bias treatment]
• Investment universe: Index membership, liquidity floor (e.g., minimum ADV), market-cap band, sector/country scope
• Benchmark and risk model: Which risk model (Barra, Axioma, in-house PCA) the factor will be orthogonalized against
• Rebalance parameters: Signal refresh frequency, portfolio rebalance cadence, and turnover constraints
• Back-test window: In-sample vs. out-of-sample date ranges; any structural-break dates to segment

Workflow

1. Specify the raw signal

   • Define the formula or computation graph (e.g., z-score of 3-month EPS revision breadth, winsorized at +/- 3σ)
   • Document every transformation: scaling, winsorization, log-transform, lag
   • State the look-back window and decay function if applicable

2. Apply universe and timing rules

   • Filter the investable universe by liquidity, listing status, and sector exclusions
   • Align signal observation dates to point-in-time data availability — no look-ahead bias
   • Define the rebalance calendar (month-end, quarter-end, event-driven)

3. Normalize and cross-section adjust

   • Cross-sectional z-score or rank-normalize within appropriate groups (sector, country, or full universe)
   • Handle missing data: drop, fill-forward with decay, or impute with group median — document the choice
   • Winsorize or truncate outliers and state thresholds

4. Orthogonalize against risk factors

   • Regress the normalized signal on the chosen risk-model exposures (e.g., Barra style/industry factors)
   • Retain the residual as the pure alpha signal
   • Record R-squared and coefficient stability across rolling windows [VERIFY orthogonalization frequency matches rebalance cadence]

5. Back-test and evaluate

   • Construct long/short quintile or decile portfolios; compute annualized return, Sharpe ratio, IC/IR, max drawdown, and turnover
   • Split in-sample (model fitting) and out-of-sample (validation) periods
   • Run decay analysis: how quickly does signal efficacy fade after formation?
   • Check for crowding proxies (short-interest, ETF overlap) that could erode the signal forward

6. Stress-test and robustness checks

   • Vary look-back windows, winsorization bounds, and rebalance frequency
   • Test across sub-periods (pre/post regime changes, volatility regimes)
   • Confirm the factor is not subsumed by existing library factors (spanning test)

7. Document the factor definition card

   • Produce a single-page factor definition card (see Output section) plus a detailed methodology appendix

Output

Factor Definition Card containing:

• Factor name and mnemonic (e.g., EPS_REV_BREADTH_3M)
• Economic rationale — one-paragraph hypothesis
• Signal formula — unambiguous mathematical or pseudo-code specification
• Universe and rebalance rules — eligibility criteria, calendar, turnover cap
• Normalization method — z-score vs. rank, group structure, outlier treatment
• Orthogonalization spec — risk model used, regression details, residual extraction
• Back-test summary table — annualized return, Sharpe, IC, IR, max drawdown, turnover (in-sample and out-of-sample)
• Decay profile — IC at T+1, T+5, T+20, T+60
• Known limitations — data gaps, regime sensitivity, crowding risk
• Version and changelog — factor version, author, date, and diffs from prior version

Quality Checks

• No look-ahead bias: Confirm every data point is available strictly before the signal observation date
• Point-in-time integrity: Verify vendor timestamps; flag any restated financials or backfilled data [VERIFY]
• Survivorship bias: Universe must include delisted securities during the back-test window
• Transaction cost realism: Apply realistic spread and market-impact estimates; confirm net-of-cost Sharpe > threshold
• Redundancy test: Correlation with existing factor library < 0.5 (or fails spanning regression) to justify inclusion
• Stability: IC sign and magnitude stable across rolling 36-month windows; no single sub-period drives aggregate result
• Reproducibility: Another quant can recreate the signal from the definition card alone, producing matching quintile returns within rounding tolerance