Company Valuation Agent Skill

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 3 method paths — pick the richest one available.

Environment status:

!`python3 -c "import yfinance, numpy, pandas; print('YFIN_OK')" 2>/dev/null || echo "YFIN_MISSING"`

!`(command -v funda && funda --version) 2>/dev/null || echo "FUNDA_CLI_MISSING"`

!`python3 -c "import yfinance as yf; t=yf.Ticker('^TNX'); p=t.fast_info.last_price; print(f'RF_10Y={p/100:.4f}')" 2>/dev/null || echo "RF_FETCH_FAIL"`

Decision tree:

| Condition | Method path |

|---|---|

| YFIN_OK | Path A (primary): yfinance for financials + peer multiples |

| YFIN_MISSING but FUNDA_CLI_MISSING is not set | Path B: delegate to finance-data-providers:funda-data skill for fundamentals |

| Both missing | Path C: 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

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:

# 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_out

Gates: (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.

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

# 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_out

Also 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 theme
• references/sotp.md — Sum-of-parts methodology, conglomerate discount detection, catalysts
• references/wacc_erp_rates.md — Risk-free rates, equity risk premiums, sector WACC benchmarks, sector-default betas