Company Valuation

# Company Valuation

Estimate the intrinsic value of a public company via DCF, relative (peer multiple), and sum-of-parts (SOTP) methods, and blend into a triangulated implied share price with sensitivity tables.

## What it does

- Pulls 5 years of financials + analyst estimates via yfinance
- Builds a 5-year DCF with explicit revenue / margin / WACC / terminal-value assumptions
- Applies peer median P/E, EV/Revenue, EV/EBITDA multiples across 4-6 peers
- Runs SOTP when the company has 2+ distinct reporting segments
- Presents a blended implied price with method weights, WACC × g sensitivity matrix, and Bull/Base/Bear scenarios
- Handles banks/REITs/pre-revenue/cyclical edge cases with appropriate fallbacks

## Triggers

`what is AAPL worth`, `valuation of NVDA`, `fair value of TSLA`, `DCF for MSFT`, `build a DCF`, `intrinsic value`, `implied share price`, `is X overvalued/undervalued`, `relative valuation`, `EV/EBITDA target`, `SOTP`, `sum of the parts`, `price target from fundamentals`, `value this company`

## Prerequisites

- Python 3.8+
- `yfinance`, `numpy`, `pandas` (auto-installed if missing)

Optional: `finance-data-providers:funda-data` skill as a fallback data source.

## Platform

CLI-based agents (Claude Code). Requires shell + pip.

## Setup

No authentication required. First run will auto-install dependencies.

## Reference Files

- `references/dcf.md` — DCF methodology, industry-specific guidance (software, retail, financials, healthcare, energy, manufacturing, CPG, telecom, REITs, streaming), common pitfalls
- `references/relative_valuation.md` — Peer selection heuristics, multiple adjustment rules, Rule of 40 for SaaS, default peer sets by theme
- `references/sotp.md` — Sum-of-parts methodology, conglomerate discount detection, catalyst framework, position sizing
- `references/wacc_erp_rates.md` — Risk-free rates (live + default), equity risk premiums, sector WACC bands, sector-default betas, terminal growth ceilings

## Output

Structured briefing with: headline verdict, snapshot, three-method summary, DCF build, peer comparison, SOTP (if applicable), sensitivity matrix, scenarios, key risks, and caveats.

## Disclaimer

For research and educational purposes only. Not financial advice.


# DCF Methodology — Detailed Reference

Expands on the summary in SKILL.md. Use this when building the DCF build table or when the user asks for industry-specific treatment.

## When DCF Is Appropriate

**Good fit:**
- Mature companies with predictable cash flows
- Companies whose revenue and margin trajectory can be estimated within a reasonable confidence band
- Strategic valuations requiring intrinsic value assessment
- Cross-checking a relative valuation

**Poor fit:**
- Pre-revenue / early-stage (no cash flow history)
- Banks, insurance (use DDM or excess return model)
- REITs (use NAV)
- Highly cyclical businesses without a clear cycle baseline — use mid-cycle earnings instead

## Projection Model (5-Year Explicit Forecast)

### Revenue projection

1. Compute historical 3–5 year CAGR.
2. Pull analyst consensus from `yfinance.Ticker.revenue_estimate`.
3. Consider industry growth, competitive position, and company guidance.
4. Project revenue for Y1–Y5, fading linearly toward terminal growth rate.

```
Revenue_t = Revenue_{t-1} × (1 + g_t)
```

### EBIT and Free Cash Flow Build

```
Revenue
- COGS                          → historical gross margin trend
= Gross Profit
- SG&A                          → historical SG&A % of revenue
- R&D                           → historical R&D % of revenue
- Other OpEx
= EBIT (Operating Income)

FCFF = EBIT × (1 − Tax Rate)
     + Depreciation & Amortization
     + Stock-Based Compensation    ← only if treating SBC as non-cash
     − Capital Expenditures
     − Change in Net Working Capital
```

### Assumption checklist (state explicitly)

| Assumption | How to derive | Typical range |
|---|---|---|
| Tax rate | Effective tax rate from historicals | 15–25% US; use statutory if unreliab