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Performance Attribution – Brinson-Fachler Method Explained
Move beyond headline returns. Understand exactly where and how value is created or destroyed, using the Brinson-Fachler performance attribution method integrated with NeoXam’s performance and PBOR capabilities.
Performance Attribution – Brinson-Fachler Method
Performance attribution answers a simple but crucial question: Why did my portfolio outperform or underperform its benchmark?The Brinson-Fachler method is a widely adopted framework for equity and multi-asset portfolios. It decomposes active return (portfolio return minus benchmark return) into intuitive components such as:
Allocation effect
Impact of being overweight or underweight in specific sectors or asset classes relative to the benchmark.
Selection effect
Impact of choosing better or worse securities than the benchmark within each sector.
Interaction effect
Combined effect when both allocation and selection differ from the benchmark.
When implemented on top of a robust data and performance infrastructure like NeoXam DataHub, Brinson-Fachler becomes a powerful daily management and reporting tool rather than a one-off analytical exercise.
For an overview of our architecture, see the DataHub Overview
What Is Performance Attribution and Why It Matters
Explain Relative Performance, Clearly
Headline returns rarely tell the full story. Brinson-Fachler attribution lets portfolio managers, risk teams, and clients see:
- Which sectors, regions, or strategies added or detracted value.
- Whether performance was driven more by top-down allocation or bottom-up stock selection.
- How consistent the strategy has been over time and across market regimes.
This level of explanation is increasingly expected in institutional reporting, client reviews, and board presentations.
Align with Investment Process and Governance
Attribution analysis also acts as a feedback loop on the investment process:
- Does the strategy actually generate alpha where the team claims (e.g., stock picking vs. macro allocation)?
- Are there unintended bets versus the benchmark?
- Are returns consistent with stated risk limits and guidelines?
Combined with DataHub’s strong governance and audit capabilities, this supports internal oversight, risk committees, and regulators.
Data Foundations in NeoXam DataHub
Brinson-Fachler attribution is only as good as the data foundation beneath it. NeoXam DataHub provides the core data services required for robust attribution:
- Centralized reference and pricing data (Security Master, benchmarks, business entities, funds & mandates) in a single source of truth.
- PBOR (Performance Book of Record) with dedicated data models and links across portfolios, sleeves, composites and benchmarks for reliable return calculations.
- Flexible performance computation supporting methodologies such as TWR, IRR, MWR and multiple frequencies and currencies.
All of this is managed through a configurable data lifecycle: acquisition, normalization, validation, derivation, Golden Copy creation and distribution.
See more on Golden Copy Management.
The Brinson-Fachler Method Explained
1. Clean, Aligned Inputs
For each analysis period, DataHub brings together:
- Portfolio holdings and trades (from IBOR/accounting sources)
- Benchmark composition and weights (from index and benchmark data domain)
- Market prices, FX rates and corporate actions for accurate return calculation
Data validation and reconciliation engines help ensure that portfolio and benchmark data are consistent before attribution is run.
2. Return Computation in PBOR
DataHub’s PBOR layer computes returns at:
- Portfolio and sub-portfolio levels (e.g. sleeves, segments).
- Benchmark and composite levels.
- Multiple frequencies (daily, monthly, yearly) and bases (gross, net of fees).
These returns form the basis for the Brinson-Fachler decomposition.
3. Brinson-Fachler Decomposition
For each segment (e.g. sector, country, asset class), the process decomposes active return into:
- Allocation effect – difference in segment weight vs benchmark, multiplied by benchmark segment return.
- Selection effect – difference in portfolio vs benchmark segment return, weighted by benchmark segment weight
- Interaction effect– the residual effect from simultaneous allocation and selection differences.
Results can be produced at:
- Single-period level (e.g. monthly contribution).
- Linked multi-period level, consistent with the return-linking conventions used in PBOR.
4. Reporting and Distribution
Attribution results can then be:
- Distributed to downstream systems via APIs, files, or database access.
- Consumed by reporting tools, including NeoXam Impress for GIPS-compliant performance reports, dashboards and client presentations.
See Performance Measurement and PBOR and Regulatory and Client Reporting for related capabilities.
Governance, History and Auditability
Performance attribution is often revisited months or years later during audits, client reviews, or regulator inspections. DataHub’s core governance features ensure that analyses remain explainable over time:
Audit trail
All changes to data, rules and configurations are logged with timestamp, user and context, with rollback capabilities.
Bi-temporality (AsAt / AsOf)
Reconstruct portfolio, benchmark and price data as it was effective on a given date and as it was observed at a given point in time. This allows you to rerun attribution “as it was seen” at the time of a report.
Entitlement management
Access to data and attribution outputs can be restricted by role, entity, portfolio or business line.
You can read more in Audit Trail and Traceability.
Typical Use Cases
Equity portfolio reviews
Monthly or quarterly Brinson-Fachler attribution for equity funds vs. their benchmarks, segmented by sector, region or style.
Multi-asset strategies
Attribution by asset class and region, highlighting whether allocation or selection drives relative performance.
Model portfolio and overlay strategies
Understanding the impact of hedging overlays or tactical tilts on the overall portfolio vs. a policy benchmark.
GIPS-compliant performance presentations
Feeding attribution outputs into GIPS reports and digital factsheets via NeoXam Impress.
Conclusion & Next Steps
By combining rigorous data management, PBOR-based return computation, and robust governance with the Brinson-Fachler performance attribution method, NeoXam DataHub delivers a complete framework to explain portfolio performance relative to benchmarks.
You do not just see that a fund outperformed or underperformed; you can demonstrate why, with clear attribution across allocation, selection and interaction effects, supported by auditable, historically reconstructable data.
Continue exploring related topics in Performance Measurement and PBOR, Golden Copy Management and Audit Trail and Traceability.
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