Performance Attribution

If you need to explain where portfolio alpha actually came from, this breaks down returns into allocation, selection, and interaction effects using Brinson-Fachler methodology. It handles equity attribution, factor-based decomposition (think Fama-French loadings), fixed-income components like yield and curve shifts, and currency effects for international portfolios. The skill covers multi-period linking methods so your attribution compounds correctly across time horizons. Good for answering "did I beat the benchmark because of sector bets or stock picking" questions with actual numbers. The worked examples show real calculations with weights and returns, which is helpful since attribution formulas get messy fast.

skills.sh installs: 705
GitHub stars: 224
last updated: Aug 21, 2026

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Install

$ npx -y skills add joellewis/finance_skills --skill performance-attribution --agent claude-code

Installs into .claude/skills of the current project.

Files

Performance Attribution

Core Concepts

Brinson-Fachler Attribution (Single Period)

The classic equity attribution model decomposes active return (portfolio return minus benchmark return) into three effects:

Allocation effect: Value added by over/underweighting sectors relative to the benchmark
- A_i = (w_p,i - w_b,i) × (R_b,i - R_b)
- Rewards overweighting sectors that outperform the total benchmark
Selection effect: Value added by picking better securities within each sector
- S_i = w_b,i × (R_p,i - R_b,i)
- Rewards outperforming the sector benchmark regardless of weight
Interaction effect: Combined effect of both overweighting and outperforming (or vice versa)
- I_i = (w_p,i - w_b,i) × (R_p,i - R_b,i)
- Captures the joint benefit of overweighting a sector AND selecting better securities in it
Total active return: R_p - R_b = Σ A_i + Σ S_i + Σ I_i

Where: w_p,i = portfolio weight in sector i, w_b,i = benchmark weight in sector i, R_p,i = portfolio return in sector i, R_b,i = benchmark return in sector i, R_b = total benchmark return.

SKILL.md on GitHub