From wealth-management
Analyzes equity securities, factor models (CAPM, Fama-French), valuation ratios (P/E, PEG, EV/EBITDA), index construction (market cap, equal, fundamental), and style analysis for stocks and portfolios.
npx claudepluginhub joellewis/finance_skills --plugin wealth-managementThis skill uses the workspace's default tool permissions.
Analyze equity securities including individual stocks, equity indices, and equity portfolios. This skill covers factor models, valuation ratios, index construction methodologies, style analysis, earnings mechanics, and sector classification frameworks essential for equity market analysis.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
Analyze equity securities including individual stocks, equity indices, and equity portfolios. This skill covers factor models, valuation ratios, index construction methodologies, style analysis, earnings mechanics, and sector classification frameworks essential for equity market analysis.
2 — Asset Classes
both
Cap-weighted indices weight each stock by its market capitalization, meaning larger companies dominate the index. Equal-weighted indices assign the same weight to every constituent, giving more influence to smaller names and requiring periodic rebalancing. Fundamental weighting uses metrics like revenue, earnings, or book value to determine weights, attempting to break the link between price and portfolio weight.
Factor models explain equity returns through systematic exposures. The single-factor CAPM uses market beta. Multi-factor models add Size (SMB — Small Minus Big), Value (HML — High Minus Low book-to-market), Momentum (UMD — Up Minus Down), Quality (profitable minus unprofitable), and Low Volatility. Factor exposures are estimated via time-series regression of excess returns on factor returns.
CAPM: E(R_i) = R_f + beta_i × (E(R_m) - R_f)
Fama-French 3-Factor: R_i - R_f = alpha + beta_m×(R_m - R_f) + beta_s×SMB + beta_v×HML + epsilon
The Morningstar style box maps funds and portfolios along two dimensions: Value/Blend/Growth (horizontal) and Large/Mid/Small cap (vertical), producing a 3x3 grid. Style is determined by valuation ratios (P/E, P/B) and growth metrics (earnings growth, sales growth). Style analysis regresses fund returns against style benchmark indices to determine effective exposures.
The Global Industry Classification Standard organizes equities into 11 sectors, 25 industry groups, 74 industries, and 163 sub-industries. Sector analysis helps identify concentration risk in portfolios and provides a framework for relative valuation comparisons.
Total Return = Price Return + Dividend Return. Price return captures capital gains only. Total return includes reinvested dividends and is the appropriate measure for performance comparison.
| Formula | Expression | Use Case |
|---|---|---|
| CAPM Expected Return | E(R_i) = R_f + beta_i × (E(R_m) - R_f) | Single-factor expected return |
| Fama-French 3-Factor | R_i - R_f = alpha + beta_m×(R_m-R_f) + beta_s×SMB + beta_v×HML + epsilon | Multi-factor return attribution |
| P/E Ratio | Price / EPS | Relative valuation |
| Earnings Yield | EPS / Price | Inverse of P/E, comparable to bond yields |
| PEG Ratio | (P/E) / Earnings Growth Rate | Growth-adjusted valuation |
| EV/EBITDA | (Market Cap + Debt - Cash) / EBITDA | Capital-structure-neutral valuation |
| Dividend Yield | Annual Dividends / Price | Income return measure |
| Total Return | Price Return + Dividend Return | Complete performance measure |
Given: beta = 1.2, R_f = 4%, E(R_m) = 10% Calculate: Expected return using CAPM Solution: Equity risk premium = E(R_m) - R_f = 10% - 4% = 6% E(R_i) = R_f + beta × ERP = 4% + 1.2 × 6% = 4% + 7.2% = 11.2%
The stock's expected return is 11.2%, reflecting a 7.2% risk premium for bearing 1.2x market risk.
Given: A fund's monthly excess returns regressed on Russell 1000 Value and Russell 1000 Growth index excess returns over 36 months. Calculate: Style tilt of the fund Solution: Regression: R_fund - R_f = alpha + beta_V×(R_Value - R_f) + beta_G×(R_Growth - R_f) + epsilon Suppose results: beta_V = 0.70, beta_G = 0.25, alpha = 0.05%/month Interpretation: The fund has a 74% value tilt (0.70 / (0.70 + 0.25)) and 26% growth tilt. The positive alpha of 0.05%/month (roughly 0.6%/year) suggests modest skill beyond style exposures. Total beta of 0.95 indicates slight cash drag.
See scripts/equities.py for computational helpers.