Company Valuation
Estimate a public company's intrinsic value with DCF, relative (peer multiple), and...
Install
npx skills add himself65/finance-skills --skill company-valuation -g -a claude-code -y- Repository
- himself65/finance-skills3.5k
- Category
- Marketing
- Updated
- 10d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.
Company Valuation
If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the ! wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.
Triangulates intrinsic value via three methods, then blends them to an implied share price:
- DCF, 5-year FCFF projection, discount at WACC, terminal value.
- Relative, apply peer median P/E, EV/Revenue, EV/EBITDA.
- SOTP, when 2+ distinct reporting segments exist, value each at pure-play peer multiples.
Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.
Disclaimer: Research/educational output. Not financial advice.
Step 1: Detection Flow
Detect data source and runtime deps. The skill supports 2 method paths, pick the richest one available.
Environment status:
!`python3 -c "exec('try:\n import yfinance, numpy, pandas\n print(\'YFIN_OK\')\nexcept Exception:\n print(\'YFIN_MISSING\')')"`
!`python3 -c "exec('try:\n import yfinance as yf\n t=yf.Ticker(\'^TNX\')\n p=t.fast_info.last_price\n print(f\'RF_10Y={p/100:.4f}\')\nexcept Exception:\n print(\'RF_FETCH_FAIL\')')"`
Decision tree:
| Condition | Method path |
|---|---|
YFIN_OK | Path A (primary): yfinance for financials + peer multiples |
YFIN_MISSING | Path B: pip-install yfinance, then Path A. python3 -m pip install -q yfinance numpy pandas |
RF_FETCH_FAIL | Use default rf = 0.045 and note stale risk-free rate in output |
If RF_10Y= printed, use that value as rf in Step 4d instead of the hardcoded 4.5%.
