Portfolio holdings, benchmark weights, returns, fundamentals, and classification data are gathered and normalized.
How FactorLens Works
Methodology
Security-level characteristics are translated into standardized style and classification exposures across the portfolio and benchmark.
Factor covariance and specific risk are combined to estimate total risk, active risk, and tracking error.
Outputs are organized into factor, sector, and stock attribution, with rolling views of active share and benchmark-relative risk over time.
Core Outputs
What FactorLens Measures
FactorLens is built around three core questions: what is your portfolio exposed to, how do those exposures differ from the benchmark, and which decisions are driving your active outcomes. The result is a benchmark-aware view of portfolio risk that links broad portfolio structure to specific holding-level drivers.
Factor, sector, and stock-level exposures show where the portfolio is tilted relative to the benchmark.
Tracking error, active share, and related statistics quantify how much the portfolio differs from its benchmark and where active risk is concentrated.
Factor, allocation, selection, and holding-level effects connect benchmark-relative results back to the portfolio decisions behind them.
Inputs And Benchmark Framework
Inputs
- Portfolio holdings and weights
- Benchmark composition and weights
- Market prices, returns, and volatility data
- Company fundamentals used in factor construction
- Sector and classification data used in attribution and risk decomposition
Benchmark-Relative Framework
Every portfolio is analyzed relative to a chosen benchmark rather than in isolation. Active exposures are defined as portfolio exposures minus benchmark exposures. Active risk and attribution are framed in benchmark-relative terms to clarify what differentiates the portfolio from its benchmark.
Decision Analysis
Attribution Methodology
FactorLens' Attribution is organized so users can move from high-level portfolio effects to the decisions driving them. Our goal is not only to show whether the portfolio outperformed or underperformed, but to explain whether those outcomes came from factor tilts, allocation decisions, or stock selection.
Sector attribution separates allocation from selection, while stock-level detail identifies the individual holdings contributing most to benchmark-relative outcomes within each classification bucket.
Shows how factor exposures contribute to active return and helps identify whether performance is being driven by systematic tilts.
Separates allocation and selection effects to show whether benchmark-relative outcomes came from sector positioning or security selection.
Traces results to individual holdings so users can see which names are driving active return, active risk, and concentration.
Use With Judgment
How To Interpret The Results
- The model estimates likely drivers of benchmark-relative risk and return, but it does not produce guaranteed forecasts.
- Results depend on holdings accuracy, benchmark choice, data coverage, and the stability of the underlying inputs.
- Outputs are designed to support portfolio decisions and monitoring, not replace investor judgment.
Key Definitions
Active share measures how different a portfolio’s holdings are from its benchmark based on differences in constituent weights.
Tracking error estimates the volatility of active returns and is commonly used as a measure of benchmark-relative risk.
Allocation reflects how the portfolio is positioned across sectors or groups, while selection reflects which securities are chosen within those groups.
The benchmark defines the baseline for active exposures, active risk, and attribution, so an inappropriate benchmark can distort the analysis.
It means the model is designed to explain a large share of cross-sectional return variation, while still leaving room for security-specific effects and model error.
See the methodology in action
Explore the demo or create an account to start monitoring benchmark-relative portfolio risk.