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
- Classify the business — sector (GICS or equivalent), cyclical vs defensive, capital intensity, leverage. This determines the valuation toolkit (table above).
- 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).
- Earnings basis — pick trailing vs forward EPS, check for one-offs, use diluted share count. For cyclicals, normalize to mid-cycle.
- 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).
- 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.
- 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:
- Capital-intensive industrial → primary metric is EV/EBITDA (table above), with P/E as cross-check.
- EV = $500M + $100M - $50M = $550M. EV/EBITDA = $550M / $75M = 7.33x.
- Cross-check: P/E = $150 / $7.50 = 20.0x; earnings yield = 7.50 / 150 = 5.0%.
- 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.
1---2name: equities3description: 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.4---5
6# Equities
7
8This 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.
9
10## Core Concepts
11
12### Choosing the Valuation Metric
13
14Match the metric to the sector and capital structure — using the wrong one is the most common equity-analysis error.
15
16| Situation | Use | Avoid | Why |
17|-----------|-----|-------|-----|
18| 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 |
19| Capital-intensive (industrials, telecom, energy) | EV/EBITDA, EV/EBIT | P/E alone | Neutralizes depreciation policy and leverage differences across peers |
20| Mature dividend payers (utilities, staples) | Dividend yield + payout sustainability, P/E | PEG | Growth is low and stable; income and coverage matter most |
21| 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 |
22| 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 |
23| Negative earnings, positive cash flow | EV/EBITDA, P/FCF | P/E, earnings yield | Ratio is undefined or misleading with negative denominator |
24| 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 |
25| Cross-border / different leverage | EV-based multiples | Equity multiples | Enterprise value normalizes for capital structure |
26
27Cross-checks that apply everywhere:
28- Use forward (next-12-month) estimates for the numerator decision when the business is changing; trailing figures when estimate quality is poor.
29- Compare against the company's own history and a true peer set, not the whole market.
30- Translate any multiple into its implied assumptions (growth, margin, required return) before declaring cheap/expensive — a low multiple usually encodes a real problem.
31
32### Choosing the Index Methodology
33
34| Mandate | Methodology | Trade-off to flag |
35|---------|-------------|-------------------|
36| 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% |
37| Reduce concentration / small-cap tilt | Equal-weighted | Higher turnover and rebalancing cost; structural size and contrarian tilt |
38| Break the price-weight link | Fundamental-weighted (revenue, earnings, book) | Effectively a value tilt with extra steps; compare cost vs an explicit value fund |
39| Explicit factor exposure | Factor/style index (value, momentum, quality, low vol) | Verify the factor definition and rebalance rules; factor timing rarely works |
40| Avoid | Price-weighted (DJIA-style) | Weight proportional to share price is economically arbitrary — legacy only |
41
42Selection 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.
43
44### Security Analysis Sequence
45
461. **Classify the business** — sector (GICS or equivalent), cyclical vs defensive, capital intensity, leverage. This determines the valuation toolkit (table above).
472. **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).
483. **Earnings basis** — pick trailing vs forward EPS, check for one-offs, use diluted share count. For cyclicals, normalize to mid-cycle.
494. **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).
505. **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.
516. **Portfolio fit** — marginal effect on sector concentration and factor tilts; total return (price + dividends) is the comparison basis, never price return alone.
52
53## Key Formulas
54
55| Formula | Expression | Use Case |
56|---------|-----------|----------|
57| EV/EBITDA | (Market Cap + Debt - Cash) / EBITDA | Capital-structure-neutral valuation |
58| Earnings Yield | EPS / Price | Compare equity vs bond yields |
59| PEG | (P/E) / Earnings Growth Rate (in %) | Growth-adjusted valuation |
60| Gordon Growth | P = D1 / (r - g) | Dividend-based intrinsic value |
61| CAPM | E(R) = R_f + beta × (E(R_m) - R_f) | Required return input for valuation |
62| Total Return | Price Return + Dividend Return | Performance comparison basis |
63
64## Worked Examples
65
66### Metric Selection and Valuation
67
68**Given:** An industrial company with market cap $500M, total debt $100M, cash $50M, EBITDA $75M, EPS $7.50, price $150.
69**Decide and calculate:**
701. Capital-intensive industrial → primary metric is EV/EBITDA (table above), with P/E as cross-check.
712. EV = $500M + $100M - $50M = $550M. EV/EBITDA = $550M / $75M = **7.33x**.
723. Cross-check: P/E = $150 / $7.50 = 20.0x; earnings yield = 7.50 / 150 = **5.0%**.
734. 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.
74
75## Common Pitfalls
76- Applying EV/EBITDA to banks or P/E to REITs — metric/sector mismatch is the dominant error this skill exists to prevent
77- Buying cyclicals on low trailing P/E at the cycle peak (the "value trap" inversion)
78- Treating a fundamental-weighted or smart-beta index as alpha rather than a packaged factor tilt
79- Confusing price return with total return — dividends compound to a large share of long-run equity returns
80- Survivorship bias in backtested factor or screen results
81
82## Cross-References
83- **historical-risk** (wealth-management plugin): volatility and drawdown measurement for equity return series
84- **statistics-fundamentals** (core plugin): beta estimation via CAPM regression
85- **performance-metrics** (wealth-management plugin): Sharpe ratio and related risk-adjusted return measures
86- **fund-vehicles** (wealth-management plugin): equity fund selection (ETFs, mutual funds, SMAs)
87- **currencies-and-fx** (wealth-management plugin): international equity currency effects
88- **asset-allocation** (wealth-management plugin): equity allocation within multi-asset portfolios
89- **real-assets** (wealth-management plugin): REIT valuation (P/FFO, NAV) is owned by that skill
90- **qualitative-valuation** (wealth-management plugin) and **quantitative-valuation** (wealth-management plugin): deeper single-company valuation workflows
91- **financial-statements** (wealth-management plugin): EBITDA, free cash flow, ROIC, and margin analysis underpinning fundamental stock selection
92- **equity-compensation** (wealth-management plugin): employer stock acquired through RSUs, options, and ESPPs carries equity risk plus tax and insider-trading constraints
93- **factor-investing** (wealth-management plugin): the factor-loading evaluation of smart-beta and style products prescribed above lives in that skill
94## Running the Script
95
96```bash
97uv run scripts/equities.py # run the demo (uses PEP 723 inline deps)
98uv run scripts/equities.py --verify # check demo outputs against the worked example (exit 1 on mismatch)
99python3 scripts/equities.py # alternative (requires: pip install numpy)
100```
101
102The 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`.
joellewis