Portfolio Waterfall Analysis

Master multi-layer performance attribution. Decompose returns into allocation, selection, interaction, and timing effects. Build the attribution systems used by the world's largest asset managers.

1.5 Days Duration
Intermediate Level
4 Layers Attribution
BUILD Exercises

Attribution Waterfall

Sample Attribution Decomposition
0.00%
Benchmark
+1.24%
Allocation
+0.87%
Selection
-0.31%
Interaction
+0.15%
Timing
-0.08%
Currency
+1.87%
Active Return
Positive Contribution
Negative Contribution
Total Active

Attribution Layers

LAYER 1
Allocation Effect
Σ (w_p - w_b) × (R_b,i - R_b)
Measures value added from over/underweighting sectors relative to benchmark. Pure allocation decisions independent of security selection. Critical for top-down vs bottom-up manager evaluation.
LAYER 2
Selection Effect
Σ w_b × (R_p,i - R_b,i)
Measures value added from picking securities within each sector. Uses benchmark weights to isolate pure stock picking skill. The core measure of fundamental research effectiveness.
LAYER 3
Interaction Effect
Σ (w_p - w_b) × (R_p,i - R_b,i)
Cross-product of allocation and selection decisions. Positive when overweight sectors with superior selection. Often misattributed; proper interpretation requires understanding decision sequence.
LAYER 4
Currency Effect
Σ w_p × (FX_spot - FX_hedge)
Decomposes currency contribution for international portfolios. Separates hedged vs unhedged currency exposure. Essential for global mandates with currency overlay decisions.

Institutional Use Cases

Manager Evaluation
Institutional allocators use attribution to assess manager skill. Persistent positive selection effect indicates genuine research edge. Allocation drift may signal style drift or unintended factor exposures.
Performance Reporting
Client reports require attribution to explain return drivers. Waterfall charts communicate value-add sources clearly. Regulatory requirements increasingly mandate attribution disclosure.
Investment Process Review
Attribution identifies process breakdowns before they compound. Negative interaction effect may indicate poor timing of allocation shifts. Enables continuous improvement of investment decision-making.
Fee Justification
Active fees require demonstrated value-add over passive alternatives. Attribution quantifies skill-based alpha separate from factor exposure. Essential for fee negotiation and mandate retention.

Lab Exercises

1
Brinson Attribution Implementation
Implement the Brinson-Fachler model from scratch. Calculate allocation, selection, and interaction effects for a multi-sector equity portfolio against the S&P 500.
Python Equities BUILD
2
Multi-Period Linking
Handle the non-additive nature of returns across periods. Implement geometric linking methods. Compare Carino, Menchero, and GRAP approaches.
Python Time Series BUILD
3
Currency Attribution
Decompose returns for a global equity portfolio with partial currency hedge. Separate local market return, hedge gain/loss, and unhedged currency exposure.
Python International BUILD
4
Fixed Income Attribution
Extend attribution to bond portfolios. Decompose into duration, yield curve, spread, and security selection effects. Handle duration-adjusted weights.
Python Fixed Income BUILD
5
Automated Reporting Pipeline
Build an end-to-end attribution pipeline. Ingest positions and returns, calculate attribution, generate waterfall visualizations, and export to PDF report.
Python Automation BUILD

Deliverable Formats

PDF Reports
Client-ready waterfall charts
Excel Templates
Auditable calculation sheets
Python Modules
Reusable attribution library
Dashboard
Interactive visualization app

Master Performance Attribution

Build the same attribution systems used by BlackRock, Vanguard, and State Street. Includes full source code and reusable templates.

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