Company Valuation

作者 himself6501fc7b4b34ae無授權條款3.3K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 天前更新

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.

僅含說明Business & Finance Data & Analytics

AI 產生的概覽

用現金流折現、同業倍數與分部估值估算上市公司內在價值，並加權得出隱含股價。

功能: 此技能對上市公司進行三種方法的估值：五年自由現金流折現模型、以同業倍數為基礎的相對估值，以及在有多個報告分部時使用的分部估值。它將各方法結果加權成一個隱含股價，並給出相對市價的上漲或下跌空間，同時提供 WACC 與永續成長率的敏感度網格，以及樂觀、基準、悲觀三種情境。最終產出結構化的估值報告，包含假設表、同業比較、敏感度矩陣與主要風險，並聲明結果僅供研究或學習參考，不構成投資建議。
適用情境: 當使用者詢問某公司或股票代號值多少錢時使用，包括公允價值、內在價值、隱含股價、以基本面推估的目標價，或判斷股價是否高估或低估。也適用於建立 DCF、推導以 EV/EBITDA 或本益比為基礎的目標價、進行同業比較或分部估值分析。
執行需求: 需要 Python 3 以及 yfinance、numpy、pandas；若缺少 yfinance 可透過 pip 安裝。需要網路連線以取得市場資料與即時十年期美國公債殖利率，取得失敗時改用預設無風險利率。分部估值所需的分部資料須由使用者提供或從財報中解析，因為 yfinance 不提供該資料。此技能未附帶指令碼，僅為說明文件。

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

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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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