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performance-attribution

Decompose portfolio excess returns into allocation, selection, and interaction effects.

Updated May 5, 2026
Search Tags:#portfolio analysis#market timing#performance attribution#brinson model#benchmark comparison#factor decomposition#risk-adjusted metrics
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill performance-attribution-wudye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-attribution
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/performance-attribution
Command: npx skills add https://github.com/wudye/traderAssistHK --skill performance-attribution-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Performance attribution explains the sources of a portfolio’s returns, turning raw performance into actionable insight on allocation, selection, factor exposure, and timing relative to a benchmark.

Core Features & Use Cases

  • Brinson allocation/selection attribution: Quantifies how sector (or group) weight differences and within-group stock picking drive excess return, including interaction effects.
  • Factor alpha/beta decomposition: Separates market exposure and factor tilts using single- or multi-factor regressions (e.g., Fama-French style factors with interpretable proxies).
  • Market-timing evaluation & benchmark comparison: Tests timing skill with models like Treynor-Mazuy and Henriksson-Merton and summarizes results with risk-adjusted metrics and rolling stability.

Example

If a strategy shows strong monthly returns versus a benchmark, use this skill to determine whether the outperformance came mainly from sector allocation, stock selection, factor tilts (alpha vs beta), or genuine timing improvements.

Quick Start

Ask for a performance attribution report for your strategy and its benchmark that breaks excess return into Brinson effects, estimates factor betas/alpha, evaluates timing using Treynor-Mazuy and Henriksson-Merton, and summarizes results with Sharpe and information ratio.

Frequently Asked Questions about performance-attribution

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I decompose portfolio excess returns into allocation and selection effects?▼

Portfolio excess returns are decomposed using the Brinson model, which quantifies how sector weight differences and within-group stock picking drive performance, including interaction effects, relative to a benchmark.

What is factor decomposition in performance attribution and how does it separate alpha?▼

Factor decomposition in performance attribution uses single- or multi-factor regressions, such as Fama-French style factors, to separate genuine alpha from market exposure and factor tilts based on benchmark and portfolio return series.

Can I evaluate market timing skill using Treynor-Mazuy and Henriksson-Merton models?▼

Yes, market timing skill is evaluated using Treynor-Mazuy and Henriksson-Merton models to calculate TM/HM gamma parameters, requiring appropriate sample sizes of portfolio and benchmark returns to assess timing ability.

How do I run a multi-period performance attribution analysis for rolling evaluation?▼

Multi-period performance attribution applies Brinson allocation and selection effects across linked single periods, enabling rolling evaluation of sector and stock-level strategies in equity and multi-asset contexts using portfolio and benchmark weights.

Do I need factor return series to calculate risk-adjusted metrics and information ratio?▼

Factor return series are specifically required for factor alpha and beta decomposition, while risk-adjusted metrics like Sharpe and information ratio summarize overall attribution results from portfolio and benchmark return data.

Why does my strategy outperform the benchmark and how can I identify the source?▼

Strategy outperformance sources are identified by breaking down excess return into Brinson allocation effects, factor tilts, alpha generation, and genuine timing improvements, turning raw performance into actionable attribution insight.

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