Grad Fama French Agent Skill
Apply the Fama-French three-factor model to decompose asset returns into market, size, and value factors. Use this skill when the user needs to explain cross-sectional return differences, evaluate fund performance beyond CAPM alpha, assess small-cap or value tilts in a portfolio, or when they ask 'why do small caps earn more', 'is value premium real', or 'what factors drive returns'.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/asgard-ai-platform/skills --skill grad-fama-frenchWhat comes with it
4 778 bytes besides the instructionThe instruction itself
13 sections, as written by the authorFama-French Three-Factor Model
Overview
Fama and French (1993) extended CAPM by adding two factors — size (SMB) and value (HML) — to explain cross-sectional variation in stock returns that CAPM alone cannot capture. The model shows that small-cap and high book-to-market stocks earn systematic premiums.
When to Use
- Explaining why CAPM alpha is nonzero for certain portfolios
- Evaluating fund manager skill after controlling for factor exposures
- Constructing factor-tilted portfolios
- Academic research on asset pricing anomalies
When NOT to Use
- For fixed income or derivatives pricing (equity-focused factors)
- When factor data is unavailable for the market in question
- As a complete model — profitability and investment factors may also matter (five-factor)
Assumptions
IRON LAW: Single-factor models (CAPM) underestimate expected returns
for small-cap and value stocks. Size and value represent systematic
risk factors that command their own premia.
Key assumptions:
- SMB and HML capture systematic risk, not mispricing
- Factor premia are persistent across time periods and markets
- Factors are constructed from observable, rebalanced portfolios
Methodology
Step 1 — Obtain Factor Data
- Rm-Rf: market excess return
- SMB (Small Minus Big): return of small-cap portfolio minus large-cap portfolio
- HML (High Minus Low): return of high B/M portfolio minus low B/M portfolio
Step 2 — Run Time-Series Regression
Ri - Rf = ai + bi(Rm-Rf) + si(SMB) + hi(HML) + ei. See references/ for construction details.
Step 3 — Interpret Factor Loadings
- bi: market sensitivity (same as CAPM beta)
- si: size exposure (positive = small-cap tilt)
- hi: value exposure (positive = value tilt, negative = growth tilt)
Step 4 — Evaluate Alpha
If alpha (ai) is statistically insignificant, returns are explained by factor exposures — no manager skill.
Output Format
## Fama-French Analysis: [Fund / Portfolio]
### Regression Results
| Factor | Loading | t-stat | Interpretation |
|--------|---------|--------|----------------|
| Market (Rm-Rf) | x.xx | x.xx | [market exposure] |
| SMB | x.xx | x.xx | [size tilt] |
| HML | x.xx | x.xx | [value tilt] |
| Alpha | x.xx% | x.xx | [skill or luck] |
### R-squared
- Three-factor R2: x% vs CAPM R2: x%
### Conclusions
- [Factor attribution summary]
- [Manager skill assessment]
Gotchas
- Factor premia vary across countries and time periods — not guaranteed to persist
- HML has weakened post-publication; some attribute this to arbitrage
- Five-factor model (2015) adds profitability (RMW) and investment (CMA) — three-factor may be insufficient
- Factor construction methodology matters; different breakpoints yield different results
- High R-squared does not mean the model is "correct" — it means factors explain variance
- Debate persists whether factors represent risk or mispricing
References
- Fama, E. & French, K. (1993). Common risk factors in the returns on stocks and bonds. *Journal of Financial Economics*, 33(1), 3-56.
- Fama, E. & French, K. (2015). A five-factor asset pricing model. *Journal of Financial Economics*, 116(1), 1-22.
- Fama, E. & French, K. (1992). The cross-section of expected stock returns. *Journal of Finance*, 47(2), 427-465.
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How to use it
Take asgard-ai-platform/grad-fama-french from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.