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equity-fund-attribution

Analyzes equity fund performance attribution using Brinson, Fama-French, and Barra factor models.

Updated Jun 22, 2026
Search Tags:#performance analysis#risk attribution#brinson#fund manager evaluation#factor regression#fama-french#fund attribution
One-click install
npx skills add https://github.com/sliec/shared-files --skill equity-fund-attribution-sliec
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: equity-fund-attribution
Source: https://github.com/sliec/shared-files/tree/main/skills/equity-fund-attribution
Command: npx skills add https://github.com/sliec/shared-files --skill equity-fund-attribution-sliec

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, statsmodels, and includes scripts (resource) and references (resource) components.

What problem does it solve? Evaluating whether a fund manager's returns come from genuine skill or just market exposure requires rigorous quantitative decomposition, which is difficult to perform manually across multiple attribution frameworks. ## Core Features & Use Cases - Three Attribution Paths: Net-value regression (T-M/H-M/C-L timing models, Fama-French 3/4/5-factor, Carhart), holdings-based attribution (Brinson AA/SS/IA, CT&CS style, Barra multi-factor), and x-sigma-rho risk decomposition. - Manager Ability Profiling: Six-module capability assessment covering investment method, asset allocation, style stability, industry allocation, stock selection, and trading ability, with star-rated output templates. - Report Generation: Structured six-section attribution reports with quantitative conclusions and qualitative cross-validation. - Use Case: Given a fund's NAV series and quarterly holdings, run the full attribution workflow to determine whether the manager's alpha comes from stock selection, industry allocation, or style timing, then produce a capability radar profile. ## Quick Start Ask the agent to run a full performance attribution analysis on an equity fund using its NAV history and holdings data, including Brinson attribution and a Carhart four-factor regression.

Frequently Asked Questions about equity-fund-attribution

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

FAQPage Schema
How do I run Brinson attribution on fund holdings?▼

Use the brinson subcommand with a holdings file and benchmark file containing category, weight, and return columns. The script decomposes excess return into allocation effect, selection effect, and interaction effect, supporting both BHB and BF schemes.

What is the difference between T-M, H-M, and C-L timing models?▼

All three test market timing ability via regression. T-M uses a squared market return term, H-M uses a bull-market dummy interaction, and C-L compares beta across up and down markets. Positive significant coefficients indicate timing skill.

How to test fund manager stock selection ability with factor models?▼

Run the factor subcommand with NAV and factor data to fit CAPM, Fama-French, or Carhart regressions. A positive, statistically significant intercept alpha after controlling for market, size, value, and momentum factors indicates genuine selection skill.

Does the attribution script support multi-period Brinson linking?▼

Yes, the script includes Carino linking for multi-period Brinson attribution, which applies a smoothing factor to each period's results so the linked attribution sums exactly to total excess return without residuals.

What data do I need for equity fund attribution analysis?▼

You need fund NAV series, benchmark index returns, factor data (SMB, HML, MOM), and optionally quarterly holdings. At least three years of data is recommended for statistically meaningful attribution conclusions.

Why might factor regression results be unreliable for fund analysis?▼

Results suffer from multicollinearity between factors, style drift over fixed windows, and short sample periods. Use rolling 24-60 month windows, check statistical significance with p-values, and cross-validate with holdings-based methods.

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