Company Valuation

by himself6501fc7b4b34aeNo license3.3K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 days ago

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.

Instructions onlyBusiness & Finance Data & Analytics

AI-generated overview

Estimates a public company's intrinsic value using DCF, peer multiples and sum-of-the-parts, blended into an implied share price.

What it does: This skill builds a three-method valuation of a public company: a five-year discounted cash flow model, a relative valuation against peer multiples, and a sum-of-the-parts valuation when multiple reporting segments exist. It blends the method outputs into an implied share price with upside or downside versus the market price, and adds a WACC by terminal-growth sensitivity grid plus bull, base and bear scenarios. It produces a structured written valuation report with assumption tables, peer comparison, sensitivity matrix and key risks, and states that the output is research or educational and not financial advice.
When to use it: Use it when a user asks what a company or ticker is worth, including fair value, intrinsic value, implied share price, a fundamentals-based price target, or whether a stock looks overvalued or undervalued. It also fits requests to build a DCF, derive EV/EBITDA or P/E based targets, compare against peers, or run a sum-of-the-parts analysis.
Requirements: Requires Python 3 with yfinance, numpy and pandas; the skill can pip-install yfinance if missing. It needs network access to fetch market data and a live 10-year Treasury yield, falling back to a default risk-free rate if that fetch fails. Segment data for sum-of-the-parts must be supplied by the user or parsed from filings, since yfinance does not expose it. No scripts ship with the skill; it is instructions only.

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, 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\')')"`

!`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

Settle every parameter before pulling data — these defaults apply unless the user overrides them.

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 yfimport numpy as npimport pandas as pd
TICKER = "AAPL"  # replacet = yf.Ticker(TICKER)
info       = t.infoincome_a   = t.income_stmtcashflow_a = t.cashflowbalance_a  = t.balance_sheetincome_q   = t.quarterly_income_stmtcashflow_q = t.quarterly_cashflow
earnings_est = t.earnings_estimaterevenue_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 0cash        = info.get("totalCash") or 0beta        = info.get("beta") or 1.0sector      = info.get("sector")industry    = info.get("industry")

import yfinance as yfimport numpy as npimport pandas as pd
TICKER = "AAPL"  # replacet = yf.Ticker(TICKER)
info       = t.infoincome_a   = t.income_stmtcashflow_a = t.cashflowbalance_a  = t.balance_sheetincome_q   = t.quarterly_income_stmtcashflow_q = t.quarterly_cashflow
earnings_est = t.earnings_estimaterevenue_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 0cash        = info.get("totalCash") or 0beta        = info.get("beta") or 1.0sector      = 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 grev = income_a.loc["Total Revenue"].dropna().iloc[::-1].values  # yfinance columns are newest-first; reverse to oldest -> newesthist_cagr = (rev[-1] / rev[0]) ** (1 / (len(rev)-1)) - 1y1 = float(revenue_est.loc["+1y", "growth"]) if "+1y" in revenue_est.index else hist_cagrg_terminal = 0.025growth_path = np.linspace(y1, g_terminal + 0.01, 5)
# 4b. Margins — 3y medianebit_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())eff_tax     = (income_a.loc["Tax Provision"] / income_a.loc["Pretax Income"]).iloc[:3].median()tax_rate    = float(min(0.30, max(0.15, eff_tax))) if pd.notna(eff_tax) else 0.21  # 3y median effective, floored 15%, capped 30%
# 4c. FCFF per yearrev_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. WACCrf, erp, kd = 0.045, 0.055, 0.055  # override rf with live value from Step 1ke = rf + beta * erpe_v = market_cap / (market_cap + total_debt)d_v = 1 - e_vwacc = e_v*ke + d_v*kd*(1 - tax_rate)
# 4e. Terminal value — compute both, use midpointtv_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/EBITDAtv_base   = 0.5 * (tv_gordon + tv_exit)
# 4f. Bridge to equitypv_fcff = sum(f / (1+wacc)**(i+1) for i, f in enumerate(fcff))pv_tv   = tv_base / (1+wacc)**5ev      = pv_fcff + pv_tvequity  = ev + cash - total_debtimplied_price_dcf = equity / shares_out

# 4a. Revenue growth path — fade from Y1 (consensus or hist CAGR) to terminal grev = income_a.loc["Total Revenue"].dropna().iloc[::-1].values  # yfinance columns are newest-first; reverse to oldest -> newesthist_cagr = (rev[-1] / rev[0]) ** (1 / (len(rev)-1)) - 1y1 = float(revenue_est.loc["+1y", "growth"]) if "+1y" in revenue_est.index else hist_cagrg_terminal = 0.025growth_path = np.linspace(y1, g_terminal + 0.01, 5)
# 4b. Margins — 3y medianebit_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())eff_tax     = (income_a.loc["Tax Provision"] / income_a.loc["Pretax Income"]).iloc[:3].median()tax_rate    = float(min(0.30, max(0.15, eff_tax))) if pd.notna(eff_tax) else 0.21  # 3y median effective, floored 15%, capped 30%
# 4c. FCFF per yearrev_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. WACCrf, erp, kd = 0.045, 0.055, 0.055  # override rf with live value from Step 1ke = rf + beta * erpe_v = market_cap / (market_cap + total_debt)d_v = 1 - e_vwacc = e_v*ke + d_v*kd*(1 - tax_rate)
# 4e. Terminal value — compute both, use midpointtv_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/EBITDAtv_base   = 0.5 * (tv_gordon + tv_exit)
# 4f. Bridge to equitypv_fcff = sum(f / (1+wacc)**(i+1) for i, f in enumerate(fcff))pv_tv   = tv_base / (1+wacc)**5ev      = pv_fcff + pv_tvequity  = ev + cash - total_debtimplied_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.

python

PEERS = ["MSFT", "ORCL", "CRM", "NOW", "SAP", "WDAY"]  # pick by industrymultiples = {}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_ttmimplied_ev_rev   = (med_ev_rev * rev_ttm - net_debt) / shares_outimplied_ev_ebit  = (med_ev_eb  * ebitda_ttm - net_debt) / shares_outimplied_price_rel = np.nanmedian([implied_pe, implied_ev_rev, implied_ev_ebit])

PEERS = ["MSFT", "ORCL", "CRM", "NOW", "SAP", "WDAY"]  # pick by industrymultiples = {}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_ttmimplied_ev_rev   = (med_ev_rev * rev_ttm - net_debt) / shares_outimplied_ev_ebit  = (med_ev_eb  * ebitda_ttm - net_debt) / shares_outimplied_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 priceif sotp_price is None:    blended = 0.5*implied_price_dcf + 0.5*implied_price_relelse:    blended = 0.4*implied_price_dcf + 0.3*implied_price_rel + 0.3*sotp_price
# 5x5 sensitivity gridwacc_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

# Blended implied priceif sotp_price is None:    blended = 0.5*implied_price_dcf + 0.5*implied_price_relelse:    blended = 0.4*implied_price_dcf + 0.3*implied_price_rel + 0.3*sotp_price
# 5x5 sensitivity gridwacc_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

Present the valuation in this order:

Headline verdict — one sentence with the blended fair value, the current price, the % upside or downside, and which method is most bullish or bearish.
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 — which assumptions move the answer most, and 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

Source and attribution

Source:himself65/finance-skillsinplugins/market-analysis/skills/company-valuationat commit01fc7b4

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Company Valuation

Company Valuation

Step 1: Detection Flow

Step 2: Choose Methods & Set Defaults

Method applicability

Defaults table

Step 3: Pull Data

Step 4: DCF Build

Step 5: Relative Valuation

Step 6: SOTP (multi-segment only)

Step 7: Triangulate, Sensitivity, Scenarios

Step 8: Respond to the User

Error handling

Caveats to include

Reference Files

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