account-research
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\"Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for
$ npx -y skills add charlieviettq/awesome-agent-skill --skill grad-capm --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/grad-capmContext preview
The summary Claude sees to decide when to auto-load this skill.
\"Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for
name: "\"grad-capm\"" description: "\"Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.\"." allowed-tools: Bash, Read, Write, Edit, Glob, Grep
CAPM (Sharpe, 1964; Lintner, 1965) establishes a linear relationship between systematic risk and expected return. The model states that the expected return on any asset equals the risk-free rate plus a premium for bearing market risk, scaled by the asset's beta.
IRON LAW: CAPM only prices SYSTEMATIC risk — diversifiable (unsystematic) risk earns NO premium. An asset's expected return depends solely on its beta with the market portfolio.
Key assumptions: 1. Investors are mean-variance optimizers with homogeneous expectations 2. A risk-free asset exists for unlimited borrowing and lending 3. Markets are frictionless — no taxes, transaction costs, or short-selling constraints 4. All assets are infinitely divisible and publicly traded
E(Ri) = Rf + Bi x (E(Rm) - Rf). See `references/derivation.md` for the derivation from mean-variance optimization.
Assets above the SML are undervalued (positive alpha); below are overvalued (negative alpha).
> ⚠️ **Decimal vs percent**: When passing values to or from the bundled script, all rates > (`risk_free`, `market_return`, `beta_contribution`, `expected_return`, `alpha`) are > **decimals** — `0.05` means 5%, NOT `5.0`. The narrative report below renders them as > percentages for humans, but never mix the two in the same JSON object.
## CAPM Analysis: [Asset / Portfolio] ### Inputs | Parameter | Value | Source | |-----------|-------|--------| | Risk-free rate (Rf) | x% | [source] | | Market return E(Rm) | x% | [source] | | Beta | x.xx | [estimation method] | ### Expected Return - E(Ri) = Rf + B x (E(Rm) - Rf) = x% ### SML Assessment - Alpha = Actual return - Expected return = x% - Interpretation: [undervalued / overvalued / fairly priced] ### Limitations in This Context - [Note any assumption violations]
| Script | Description | Usage | |--------|-------------|-------| | `scripts/capm.py` | Compute CAPM expected return and alpha | `python scripts/capm.py --help` |
Run `python scripts/capm.py --verify` to execute built-in sanity tests.
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