equities

name

equities

description

Analyze equity securities, factor models, and equity portfolio construction. Use when the user asks about stocks, equity valuation ratios, index construction methods, or style analysis. Also trigger when users mention 'P/E ratio', 'growth vs value', 'market cap weighting', 'sector allocation', 'GICS classification', 'earnings per share', 'Fama-French factors', 'CAPM', 'dividend yield', 'PEG ratio', 'EV/EBITDA', or ask which factors explain equity returns.

Equities

This skill is a decision procedure: which valuation metric to use for which company, which index methodology fits which mandate, and the order of operations for analyzing a stock. It assumes the user can look up definitions; the value here is choosing the right tool.

Core Concepts

Choosing the Valuation Metric

Match the metric to the sector and capital structure — using the wrong one is the most common equity-analysis error.

Situation	Use	Avoid	Why
Financials (banks, insurers)	P/B, P/TBV, ROE vs P/B	EV/EBITDA	Debt is raw material, not financing — EV and EBITDA are meaningless; book value is marked closer to fair value
Capital-intensive (industrials, telecom, energy)	EV/EBITDA, EV/EBIT	P/E alone	Neutralizes depreciation policy and leverage differences across peers
Mature dividend payers (utilities, staples)	Dividend yield + payout sustainability, P/E	PEG	Growth is low and stable; income and coverage matter most
High-growth, low/no earnings	EV/Sales, PEG (if earnings exist), unit economics	P/E, P/B	Earnings are depressed by reinvestment; book value is mostly intangibles
Cyclicals (autos, semis, materials)	Mid-cycle or normalized P/E, P/B at trough	Spot P/E	P/E is lowest at the cycle peak and highest at the trough — spot P/E inverts the buy/sell signal
Negative earnings, positive cash flow	EV/EBITDA, P/FCF	P/E, earnings yield	Ratio is undefined or misleading with negative denominator
REITs and listed real estate	P/FFO, P/AFFO, NAV	P/E	GAAP depreciation distorts earnings for property — handled in detail by the real-assets skill
Cross-border / different leverage	EV-based multiples	Equity multiples	Enterprise value normalizes for capital structure

Cross-checks that apply everywhere:

• Use forward (next-12-month) estimates for the numerator decision when the business is changing; trailing figures when estimate quality is poor.
• Compare against the company's own history and a true peer set, not the whole market.
• Translate any multiple into its implied assumptions (growth, margin, required return) before declaring cheap/expensive — a low multiple usually encodes a real problem.

Choosing the Index Methodology

Mandate	Methodology	Trade-off to flag
Cheap, tax-efficient market exposure	Cap-weighted (S&P 500, total market)	Momentum-chasing by construction; concentration in mega-caps — a single sector can exceed 30%
Reduce concentration / small-cap tilt	Equal-weighted	Higher turnover and rebalancing cost; structural size and contrarian tilt
Break the price-weight link	Fundamental-weighted (revenue, earnings, book)	Effectively a value tilt with extra steps; compare cost vs an explicit value fund
Explicit factor exposure	Factor/style index (value, momentum, quality, low vol)	Verify the factor definition and rebalance rules; factor timing rarely works
Avoid	Price-weighted (DJIA-style)	Weight proportional to share price is economically arbitrary — legacy only

Selection rules: default to cap-weighted for core beta; add equal- or fundamental-weighted only when the user explicitly wants the embedded tilt and accepts the turnover; treat any "smart beta" product as a factor portfolio and evaluate its factor loadings, not its marketing name.

Security Analysis Sequence

1. Classify the business — sector (GICS or equivalent), cyclical vs defensive, capital intensity, leverage. This determines the valuation toolkit (table above).
2. Quality screen — revenue trend, margin trend, ROIC vs cost of capital, balance-sheet risk (net debt/EBITDA, interest coverage), share count trajectory (dilution vs buybacks).
3. Earnings basis — pick trailing vs forward EPS, check for one-offs, use diluted share count. For cyclicals, normalize to mid-cycle.
4. Value with the matched metric — primary multiple from the table, one cross-check multiple, and where dividends are central a dividend-based check (Gordon growth: P = D1 / (r - g), valid only when g < r).
5. Factor and style context — regress (or eyeball) exposures to market beta, size, value, momentum, quality. Distinguish stock-specific thesis from a factor bet you could buy more cheaply via an index.
6. Portfolio fit — marginal effect on sector concentration and factor tilts; total return (price + dividends) is the comparison basis, never price return alone.

Key Formulas

Formula	Expression	Use Case
EV/EBITDA	(Market Cap + Debt - Cash) / EBITDA	Capital-structure-neutral valuation
Earnings Yield	EPS / Price	Compare equity vs bond yields
PEG	(P/E) / Earnings Growth Rate (in %)	Growth-adjusted valuation
Gordon Growth	P = D1 / (r - g)	Dividend-based intrinsic value
CAPM	E(R) = R_f + beta × (E(R_m) - R_f)	Required return input for valuation
Total Return	Price Return + Dividend Return	Performance comparison basis

Worked Examples

Metric Selection and Valuation

Given: An industrial company with market cap $500M, total debt $100M, cash $50M, EBITDA $75M, EPS $7.50, price $150. Decide and calculate:

1. Capital-intensive industrial → primary metric is EV/EBITDA (table above), with P/E as cross-check.
2. EV = $500M + $100M - $50M = $550M. EV/EBITDA = $550M / $75M = 7.33x.
3. Cross-check: P/E = $150 / $7.50 = 20.0x; earnings yield = 7.50 / 150 = 5.0%.
4. Interpretation: 7.33x EV/EBITDA is modest for an industrial if margins are stable — compare against the peer set and the company's own 5-10 year range. The 20x P/E looks richer than the EV multiple because the company carries little net debt; the EV multiple is the better cross-peer comparison.

Common Pitfalls

• Applying EV/EBITDA to banks or P/E to REITs — metric/sector mismatch is the dominant error this skill exists to prevent
• Buying cyclicals on low trailing P/E at the cycle peak (the "value trap" inversion)
• Treating a fundamental-weighted or smart-beta index as alpha rather than a packaged factor tilt
• Confusing price return with total return — dividends compound to a large share of long-run equity returns
• Survivorship bias in backtested factor or screen results

Cross-References

• historical-risk (wealth-management plugin): volatility and drawdown measurement for equity return series
• statistics-fundamentals (core plugin): beta estimation via CAPM regression
• performance-metrics (wealth-management plugin): Sharpe ratio and related risk-adjusted return measures
• fund-vehicles (wealth-management plugin): equity fund selection (ETFs, mutual funds, SMAs)
• currencies-and-fx (wealth-management plugin): international equity currency effects
• asset-allocation (wealth-management plugin): equity allocation within multi-asset portfolios
• real-assets (wealth-management plugin): REIT valuation (P/FFO, NAV) is owned by that skill
• qualitative-valuation (wealth-management plugin) and quantitative-valuation (wealth-management plugin): deeper single-company valuation workflows
• financial-statements (wealth-management plugin): EBITDA, free cash flow, ROIC, and margin analysis underpinning fundamental stock selection
• equity-compensation (wealth-management plugin): employer stock acquired through RSUs, options, and ESPPs carries equity risk plus tax and insider-trading constraints
• factor-investing (wealth-management plugin): the factor-loading evaluation of smart-beta and style products prescribed above lives in that skill

Running the Script

uv run scripts/equities.py            # run the demo (uses PEP 723 inline deps)
uv run scripts/equities.py --verify   # check demo outputs against the worked example (exit 1 on mismatch)
python3 scripts/equities.py            # alternative (requires: pip install numpy)

The demo prints valuation metrics (including the worked example's EV/EBITDA and earnings yield), a factor regression on synthetic data, and sector concentration analysis. Run --help for a list of the classes and functions. For programmatic use, import the module rather than running it — the demo only executes under python equities.py.