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Company Valuation
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%.
Step 2: Choose Methods & Set Defaults
Method applicability
| Company type | DCF | Relative | SOTP | Fallback |
|---|---|---|---|---|
| Mature cash-flow (CPG, telecom, utilities) | ✅ primary | ✅ | ❌ | — |
| High-growth SaaS / software | ✅ with care | ✅ primary | ❌ | Use EV/Revenue + Rule of 40 |
| Multi-segment conglomerate | ✅ | ✅ | ✅ primary | See references/sotp.md |
| Banks / insurance | ❌ | ✅ (P/B, P/TBV) | ❌ | DDM or excess return; note in output |
| Pre-revenue | ❌ | EV/Revenue only | ❌ | Flag low confidence |
| REITs | ❌ | ✅ (P/FFO, P/AFFO) | ❌ | NAV-based |
| Cyclicals (energy, semis, industrials) | ✅ on mid-cycle | ✅ | sometimes | Normalize through-cycle |
Defaults table
Every parameter below MUST have a value before moving to Step 3. Use these unless the user overrides.
| Parameter | Default | Rationale |
|---|---|---|
| Projection horizon | 5 years | Standard explicit forecast window |
Terminal growth g | 2.5% | ~ long-run US GDP |
Risk-free rate rf | Live 10Y UST from Step 1, else 4.5% | Current cost of capital anchor |
Equity risk premium erp | 5.5% | Damodaran mid-range |
| Beta | info['beta'] from yfinance | Market-observed levered beta |
Cost of debt kd | interest_expense / total_debt, else 5.5% | Effective rate; fallback to IG spread |
| Tax rate | 3-yr median effective rate, floored 15%, capped 30% | Strips out one-offs |
| Margin assumptions | 3-yr median of each ratio | Smooths cyclical noise |
| SBC treatment | Cash for software/SaaS; non-cash for industrials/CPG | Industry convention |
| Peer count | 4-6 | Balances signal vs noise |
| Peer multiple | Median (not mean) | Robust to outliers |
| Method weights (no SOTP) | DCF 50% / Relative 50% | Equal triangulation |
| Method weights (with SOTP) | DCF 40% / Relative 30% / SOTP 30% | SOTP gets weight when applicable |
| Sensitivity grid | WACC ±1% in 0.5% steps × g from 1.5-3.5% in 0.5% | 5×5 matrix |
See references/wacc_erp_rates.md for current risk-free rates, ERP tables, and sector WACC benchmarks.
Step 3: Pull Data
python
import yfinance as yf
import numpy as np
import pandas as pd
TICKER = "AAPL" # replace
t = yf.Ticker(TICKER)
info = t.info
income_a = t.income_stmt
cashflow_a = t.cashflow
balance_a = t.balance_sheet
income_q = t.quarterly_income_stmt
cashflow_q = t.quarterly_cashflow
earnings_est = t.earnings_estimate
revenue_est = t.revenue_estimate
price = info.get("currentPrice") or info.get("regularMarketPrice")
market_cap = info.get("marketCap")
shares_out = info.get("sharesOutstanding")
total_debt = info.get("totalDebt") or 0
cash = info.get("totalCash") or 0
beta = info.get("beta") or 1.0
sector = info.get("sector")
industry = info.get("industry")Key financial statement rows (yfinance labels):
| Need | Row |
|---|---|
| Revenue | Total Revenue |
| EBIT | Operating Income |
| Net income | Net Income |
| D&A | Depreciation And Amortization (in cashflow) |
| CapEx | Capital Expenditure (negative) |
| ΔNWC | Change In Working Capital (cashflow) |
| SBC | Stock Based Compensation (cashflow) |
Step 4: DCF Build
Full methodology + industry-specific tweaks in references/dcf.md. Quick skeleton:
python
# 4a. Revenue growth path — fade from Y1 (consensus or hist CAGR) to terminal g
hist_cagr = (rev[-1] / rev[0]) ** (1 / (len(rev)-1)) - 1
y1 = float(revenue_est.loc["+1y", "growth"]) if "+1y" in revenue_est.index else hist_cagr
g_terminal = 0.025
growth_path = np.linspace(y1, g_terminal + 0.01, 5)
# 4b. Margins — 3y median
ebit_margin = float((income_a.loc["Operating Income"] / income_a.loc["Total Revenue"]).iloc[:3].median())
da_pct = float((cashflow_a.loc["Depreciation And Amortization"] / income_a.loc["Total Revenue"]).iloc[:3].median())
capex_pct = float((cashflow_a.loc["Capital Expenditure"].abs() / income_a.loc["Total Revenue"]).iloc[:3].median())
nwc_pct = float((cashflow_a.loc["Change In Working Capital"].abs() / income_a.loc["Total Revenue"]).iloc[:3].median())
tax_rate = max(0.15, min(0.30, 0.21)) # use effective if available
# 4c. FCFF per year
rev_t = [float(income_a.loc["Total Revenue"].iloc[0])]
fcff = []
for g in growth_path:
rev_t.append(rev_t[-1] * (1 + g))
ebit = rev_t[-1] * ebit_margin
nopat = ebit * (1 - tax_rate)
fcff.append(nopat + rev_t[-1]*da_pct - rev_t[-1]*capex_pct - rev_t[-1]*nwc_pct)
# 4d. WACC
rf, erp, kd = 0.045, 0.055, 0.055 # override rf with live value from Step 1
ke = rf + beta * erp
e_v = market_cap / (market_cap + total_debt)
d_v = 1 - e_v
wacc = e_v*ke + d_v*kd*(1 - tax_rate)
# 4e. Terminal value — compute both, use midpoint
tv_gordon = fcff[-1] * (1 + g_terminal) / (wacc - g_terminal)
tv_exit = (rev_t[-1] * ebit_margin + rev_t[-1] * da_pct) * 15 # peer median EV/EBITDA
tv_base = 0.5 * (tv_gordon + tv_exit)
# 4f. Bridge to equity
pv_fcff = sum(f / (1+wacc)**(i+1) for i, f in enumerate(fcff))
pv_tv = tv_base / (1+wacc)**5
ev = pv_fcff + pv_tv
equity = ev + cash - total_debt
implied_price_dcf = equity / shares_outGates: (a) if wacc <= g_terminal → stop, g too aggressive; (b) if pv_tv / ev > 0.85 or < 0.45 → flag and show both TV methods; (c) if wacc is outside the sector sanity band in references/wacc_erp_rates.md → note.
Step 5: Relative Valuation
Select 4-6 peers. Peer map and adjustment rules in references/relative_valuation.md.
python
PEERS = ["MSFT", "ORCL", "CRM", "NOW", "SAP", "WDAY"] # pick by industry
multiples = {}
for p in PEERS:
pi = yf.Ticker(p).info
multiples[p] = {
"pe_fwd": pi.get("forwardPE"),
"ev_rev": pi.get("enterpriseToRevenue"),
"ev_ebitda": pi.get("enterpriseToEbitda"),
"ps": pi.get("priceToSalesTrailing12Months"),
}
med_pe = np.nanmedian([v["pe_fwd"] for v in multiples.values()])
med_ev_rev = np.nanmedian([v["ev_rev"] for v in multiples.values()])
med_ev_eb = np.nanmedian([v["ev_ebitda"] for v in multiples.values()])
eps_ttm = float(income_q.loc["Diluted EPS"].iloc[:4].sum())
rev_ttm = float(income_q.loc["Total Revenue"].iloc[:4].sum())
ebitda_ttm = float(income_q.loc["EBIT"].iloc[:4].sum()) + float(cashflow_q.loc["Depreciation And Amortization"].iloc[:4].sum())
net_debt = total_debt - cash
implied_pe = med_pe * eps_ttm
implied_ev_rev = (med_ev_rev * rev_ttm - net_debt) / shares_out
implied_ev_ebit = (med_ev_eb * ebitda_ttm - net_debt) / shares_out
implied_price_rel = np.nanmedian([implied_pe, implied_ev_rev, implied_ev_ebit])Adjust peer median ±10-30% if target's growth or margin profile diverges materially. Always state the adjustment and reason. Rule of 40 anchor for SaaS in references/relative_valuation.md.
Step 6: SOTP (multi-segment only)
Skip unless the 10-K reports 2+ operating segments with distinct economics. yfinance does NOT expose segment data — user must supply or parse from filings. Full methodology in references/sotp.md:
- Identify segments + pure-play peer for each
- Apply peer median EV/EBITDA (or EV/Rev for growth segments)
- Subtract unallocated corporate costs (cap 2-5% of revenue if unknown)
- Subtract net debt, minority interest; divide by shares
SOTP discount = (SOTP price − market price) / SOTP price. Flag if >20% (conglomerate discount).
Step 7: Triangulate, Sensitivity, Scenarios
python
# Blended implied price
if sotp_price is None:
blended = 0.5*implied_price_dcf + 0.5*implied_price_rel
else:
blended = 0.4*implied_price_dcf + 0.3*implied_price_rel + 0.3*sotp_price
# 5x5 sensitivity grid
wacc_grid = [wacc + dx for dx in (-0.01, -0.005, 0, 0.005, 0.01)]
g_grid = [0.015, 0.020, 0.025, 0.030, 0.035]
sens = {}
for w in wacc_grid:
for g in g_grid:
tv = fcff[-1]*(1+g)/(w-g)
pv = sum(f/(1+w)**(i+1) for i,f in enumerate(fcff)) + tv/(1+w)**5
sens[(w,g)] = (pv + cash - total_debt) / shares_outAlso produce Bull / Base / Bear: shift revenue growth ±300bps, EBIT margin ±200bps, WACC ∓100bps, terminal g 3.0% / 2.5% / 1.5%.
Step 8: Respond to the User
Output in this order:
- Headline verdict — one sentence: blended fair value, vs. current, % upside/downside, most bullish/bearish method. Example: "AAPL fair value ≈ $215 (blended), vs. current $198 → ~9% upside; DCF is most bullish at $228."
- Snapshot — sector, industry, market cap, current price, 3M / 12M price change, LTM revenue growth.
- Three-method summary — 3-column table: method | implied price | weight | brief rationale.
- DCF build — assumptions table (growth path, margins, WACC components, terminal method) + 5-yr FCFF projection table + EV-to-equity bridge.
- Peer comparison — table of peers with P/E fwd, EV/Rev, EV/EBITDA, gross margin, rev growth; bottom row = median; flag target's premium/discount.
- SOTP (if applicable) — segment table + adjustments + equity value.
- Sensitivity matrix — WACC × g grid (5×5), base case highlighted.
- Scenarios — Bull / Base / Bear table with levers + implied price.
- Key risks — 3-5 bullets: which assumption moves the answer most; what could break the thesis.
Error handling
| Missing / edge case | Action |
|---|---|
yfinance returns None for beta | Use sector-default beta from references/wacc_erp_rates.md |
| Negative LTM EBITDA | Skip EV/EBITDA multiple; rely on EV/Revenue + DCF |
| Negative LTM EPS | Skip P/E multiple; use forward P/E if positive, else skip |
| Growth > WACC in Gordon | Cap g = wacc − 0.5% and flag |
| Fewer than 3 years history | Use what's available; flag data confidence as "low" |
| Peer data fetch fails | Drop that peer from median; note in output |
| No segment data for SOTP | Skip Section 6; proceed with DCF + Relative only |
Caveats to include
- TTM data lags real-time; peer multiples reflect market sentiment (can overshoot)
- DCF is garbage-in/garbage-out; sensitivity matters more than a point estimate
- yfinance data is unofficial; cross-check any decision with primary filings
- Not financial advice
Reference Files
references/dcf.md— DCF methodology + industry-specific guidance (software, retail, financials, healthcare, energy, manufacturing, CPG, telecom, REITs, streaming)references/relative_valuation.md— Peer selection, multiple adjustment rules, Rule of 40, peer sets by themereferences/sotp.md— Sum-of-parts methodology, conglomerate discount detection, catalystsreferences/wacc_erp_rates.md— Risk-free rates, equity risk premiums, sector WACC benchmarks, sector-default betas
