company-valuation

Name: company-valuation
Author: himself65

1.2kinstalls351forks

How to install company-valuation?

himself65/finance-skills/company-valuation

$npx -y skills add himself65/finance-skills --skill company-valuation

Installs into the current project.

Summary

Estimate the intrinsic value of a public company using DCF, relative (peer multiple) and sum-of-parts (SOTP) methods, then triangulate to an implied share price with upside/downside versus the current market price. Use this skill whenever the user asks: "what is AAPL worth", "val

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SKILL.md

1	# Company Valuation
2
3	Triangulates intrinsic value via three methods, then blends them to an implied share price:
4
5	1. DCF — 5-year FCFF projection, discount at WACC, terminal value.
6	2. Relative — apply peer median P/E, EV/Revenue, EV/EBITDA.
7	3. SOTP — when 2+ distinct reporting segments exist, value each at pure-play peer multiples.
8
9	Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.
10
11	Disclaimer: Research/educational output. Not financial advice.
12
13	---
14
15	## Step 1: Detection Flow
16
17	Detect data source and runtime deps. The skill supports 3 method paths — pick the richest one available.
18
19	Environment status:
20
21	```
22	!`python3 -c "import yfinance, numpy, pandas; print('YFIN_OK')" 2>/dev/null \|\| echo "YFIN_MISSING"`
23	```
24
25	```
26	!`(command -v funda && funda --version) 2>/dev/null \|\| echo "FUNDA_CLI_MISSING"`
27	```
28
29	```
30	!`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"`
31	```
32
33	Decision tree:
34
35	\| Condition \| Method path \|
36	\|---\|---\|
37	\| `YFIN_OK` \| Path A (primary): yfinance for financials + peer multiples \|
38	\| `YFIN_MISSING` but `FUNDA_CLI_MISSING` is not set \| Path B: delegate to `finance-data-providers:funda-data` skill for fundamentals \|
39	\| Both missing \| Path C: pip-install yfinance, then Path A. `python3 -m pip install -q yfinance numpy pandas` \|
40	\| `RF_FETCH_FAIL` \| Use default `rf = 0.045` and note stale risk-free rate in output \|
41
42	If `RF_10Y=` printed, use that value as `rf` in Step 4d instead of the hardcoded 4.5%.
43
44	---
45
46	## Step 2: Choose Methods & Set Defaults
47
48	### Method applicability
49
50	\| Company type \| DCF \| Relative \| SOTP \| Fallback \|
51	\|---\|---\|---\|---\|---\|
52	\| Mature cash-flow (CPG, telecom, utilities) \| ✅ primary \| ✅ \| ❌ \| — \|
53	\| High-growth SaaS / software \| ✅ with care \| ✅ primary \| ❌ \| Use EV/Revenue + Rule of 40 \|
54	\| Multi-segment conglomerate \| ✅ \| ✅ \| ✅ primary \| See `references/sotp.md` \|
55	\| Banks / insurance \| ❌ \| ✅ (P/B, P/TBV) \| ❌ \| DDM or excess return; note in output \|
56	\| Pre-revenue \| ❌ \| EV/Revenue only \| ❌ \| Flag low confidence \|
57	\| REITs \| ❌ \| ✅ (P/FFO, P/AFFO) \| ❌ \| NAV-based \|
58	\| Cyclicals (energy, semis, industrials) \| ✅ on mid-cycle \| ✅ \| sometimes \| Normalize through-cycle \|
59
60	### Defaults table
61
62	Every parameter below MUST have a value before moving to Step 3. Use these unless the user overrides.
63
64	\| Parameter \| Default \| Rationale \|
65	\|---\|---\|---\|
66	\| Projection horizon \| 5 years \| Standard explicit forecast window \|
67	\| Terminal growth `g` \| 2.5% \| ~ long-run US GDP \|
68	\| Risk-free rate `rf` \| Live 10Y UST from Step 1, else 4.5% \| Current cost of capital anchor \|
69	\| Equity risk premium `erp` \| 5.5% \| Damodaran mid-range \|
70	\| Beta \| `info['beta']` from yfinance \| Market-observed levered beta \|
71	\| Cost of debt `kd` \| `interest_expense / total_debt`, else 5.5% \| Effective rate; fallback to IG spread \|
72	\| Tax rate \| 3-yr median effective rate, floored 15%, capped 30% \| Strips out one-offs \|
73	\| Margin assumptions \| 3-yr median of each ratio \| Smooths cyclical noise \|
74	\| SBC treatment \| Cash for software/SaaS; non-cash for industrials/CPG \| Industry convention \|
75	\| Peer count \| 4-6 \| Balances signal vs noise \|
76	\| Peer multiple \| Median (not mean) \| Robust to outliers \|
77	\| Method weights (no SOTP) \| DCF 50% / Relative 50% \| Equal triangulation \|
78	\| Method weights (with SOTP) \| DCF 40% / Relative 30% / SOTP 30% \| SOTP gets weight when applicable \|
79	\| Sensitivity grid \| WACC ±1% in 0.5% steps × g from 1.5-3.5% in 0.5% \| 5×5 matrix \|
80
81	See `references/wacc_erp_rates.md` for current risk-free rates, ERP tables, and sector WACC benchmarks.
82
83	---
84
85	## Step 3: Pull Data
86
87	```python
88	import yfinance as yf
89	import numpy as np
90	import pandas as pd
91
92	TICKER = "AAPL" # replace
93	t = yf.Ticker(TICKER)
94
95	info = t.info
96	income_a = t.income_stmt
97	cashflow_a = t.cashflow
98	balance_a = t.balance_sheet
99	income_q = t.quarterly_income_stmt
100	cashflow_q = t.quarterly_

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Type

company-valuation

1.2kinstalls351forks

View on GitHub

How to install company-valuation?

himself65/finance-skills/company-valuation

$npx -y skills add himself65/finance-skills --skill company-valuation

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Company Valuation
2
3	Triangulates intrinsic value via three methods, then blends them to an implied share price:
4
5	1. DCF — 5-year FCFF projection, discount at WACC, terminal value.
6	2. Relative — apply peer median P/E, EV/Revenue, EV/EBITDA.
7	3. SOTP — when 2+ distinct reporting segments exist, value each at pure-play peer multiples.
8
9	Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.
10
11	Disclaimer: Research/educational output. Not financial advice.
12
13	---
14
15	## Step 1: Detection Flow
16
17	Detect data source and runtime deps. The skill supports 3 method paths — pick the richest one available.
18
19	Environment status:
20
21	```
22	!`python3 -c "import yfinance, numpy, pandas; print('YFIN_OK')" 2>/dev/null \|\| echo "YFIN_MISSING"`
23	```
24
25	```
26	!`(command -v funda && funda --version) 2>/dev/null \|\| echo "FUNDA_CLI_MISSING"`
27	```
28
29	```
30	!`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"`
31	```
32
33	Decision tree:
34
35	\| Condition \| Method path \|
36	\|---\|---\|
37	\| `YFIN_OK` \| Path A (primary): yfinance for financials + peer multiples \|
38	\| `YFIN_MISSING` but `FUNDA_CLI_MISSING` is not set \| Path B: delegate to `finance-data-providers:funda-data` skill for fundamentals \|
39	\| Both missing \| Path C: pip-install yfinance, then Path A. `python3 -m pip install -q yfinance numpy pandas` \|
40	\| `RF_FETCH_FAIL` \| Use default `rf = 0.045` and note stale risk-free rate in output \|
41
42	If `RF_10Y=` printed, use that value as `rf` in Step 4d instead of the hardcoded 4.5%.
43
44	---
45
46	## Step 2: Choose Methods & Set Defaults
47
48	### Method applicability
49
50	\| Company type \| DCF \| Relative \| SOTP \| Fallback \|
51	\|---\|---\|---\|---\|---\|
52	\| Mature cash-flow (CPG, telecom, utilities) \| ✅ primary \| ✅ \| ❌ \| — \|
53	\| High-growth SaaS / software \| ✅ with care \| ✅ primary \| ❌ \| Use EV/Revenue + Rule of 40 \|
54	\| Multi-segment conglomerate \| ✅ \| ✅ \| ✅ primary \| See `references/sotp.md` \|
55	\| Banks / insurance \| ❌ \| ✅ (P/B, P/TBV) \| ❌ \| DDM or excess return; note in output \|
56	\| Pre-revenue \| ❌ \| EV/Revenue only \| ❌ \| Flag low confidence \|
57	\| REITs \| ❌ \| ✅ (P/FFO, P/AFFO) \| ❌ \| NAV-based \|
58	\| Cyclicals (energy, semis, industrials) \| ✅ on mid-cycle \| ✅ \| sometimes \| Normalize through-cycle \|
59
60	### Defaults table
61
62	Every parameter below MUST have a value before moving to Step 3. Use these unless the user overrides.
63
64	\| Parameter \| Default \| Rationale \|
65	\|---\|---\|---\|
66	\| Projection horizon \| 5 years \| Standard explicit forecast window \|
67	\| Terminal growth `g` \| 2.5% \| ~ long-run US GDP \|
68	\| Risk-free rate `rf` \| Live 10Y UST from Step 1, else 4.5% \| Current cost of capital anchor \|
69	\| Equity risk premium `erp` \| 5.5% \| Damodaran mid-range \|
70	\| Beta \| `info['beta']` from yfinance \| Market-observed levered beta \|
71	\| Cost of debt `kd` \| `interest_expense / total_debt`, else 5.5% \| Effective rate; fallback to IG spread \|
72	\| Tax rate \| 3-yr median effective rate, floored 15%, capped 30% \| Strips out one-offs \|
73	\| Margin assumptions \| 3-yr median of each ratio \| Smooths cyclical noise \|
74	\| SBC treatment \| Cash for software/SaaS; non-cash for industrials/CPG \| Industry convention \|
75	\| Peer count \| 4-6 \| Balances signal vs noise \|
76	\| Peer multiple \| Median (not mean) \| Robust to outliers \|
77	\| Method weights (no SOTP) \| DCF 50% / Relative 50% \| Equal triangulation \|
78	\| Method weights (with SOTP) \| DCF 40% / Relative 30% / SOTP 30% \| SOTP gets weight when applicable \|
79	\| Sensitivity grid \| WACC ±1% in 0.5% steps × g from 1.5-3.5% in 0.5% \| 5×5 matrix \|
80
81	See `references/wacc_erp_rates.md` for current risk-free rates, ERP tables, and sector WACC benchmarks.
82
83	---
84
85	## Step 3: Pull Data
86
87	```python
88	import yfinance as yf
89	import numpy as np
90	import pandas as pd
91
92	TICKER = "AAPL" # replace
93	t = yf.Ticker(TICKER)
94
95	info = t.info
96	income_a = t.income_stmt
97	cashflow_a = t.cashflow
98	balance_a = t.balance_sheet
99	income_q = t.quarterly_income_stmt
100	cashflow_q = t.quarterly_

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