Company Valuation

Company Valuation

Triangulates intrinsic value via three methods, then blends them to an implied share price:

1. DCF — 5-year FCFF projection, discount at WACC, terminal value.
2. Relative — apply peer median P/E, EV/Revenue, EV/EBITDA.
3. 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:

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

Defaults table

Every parameter below MUST have a value before moving to Step 3. Use these unless the user overrides.

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):

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