/bayesian-intrinsic-growth-valuation

name: bayesian-intrinsic-growth-valuation
description: Use a Bayesian intrinsic-growth valuation model to evaluate whether a company's market value sufficiently, excessively, or insufficiently prices its real 3-5 year growth. Use when the user asks for Bayesian valuation, intrinsic growth rate, implied growth, growth-hypothesis probabilities, FOMO versus fundamentals, or company analysis based on fundamentals, industry cycle, TAM, market share, margin, valuation multiples, and new information.

Bayesian Intrinsic Growth Valuation

Core Principle

Do not classify company news as simply bullish or bearish. Translate every company-specific data point into a probability update for future 3-5 year revenue growth, margin, TAM, market share, valuation multiple, and market sentiment.

The goal is to estimate the company's true intrinsic growth speed and compare it with the growth already implied by the current market value.

Treat outputs as research hypotheses, not personalized investment advice. Verify current market cap, price, revenue, margins, filings, guidance, peer multiples, and news from reliable current sources before making time-sensitive claims.

Required Inputs

Use whatever the user provides, and clearly mark missing variables that require verification:

• company fundamentals: revenue scale, margins, free cash flow, ROIC, balance sheet, customers, moat, pricing power
• industry cycle: demand growth, supply-demand gap, inventory cycle, order cycle, price trends, policy, downstream capex
• revenue and growth: historical growth, guidance, backlog, book-to-bill, organic growth, ASP, shipment volume
• TAM and TAM growth: current TAM, future TAM CAGR, penetration, market share, new market expansion
• valuation: EV/Sales, EV/EBITDA, P/E, FCF yield, PEG, historical percentile, peer percentile, implied growth
• share-price trend: 1M/3M/6M/12M and post-earnings returns, drawdown/rebound path, volatility, volume, relative performance versus sector/index, and whether price appreciation is ahead of intrinsic growth
• market FOMO: share-price move, options activity, social heat, analyst revisions, theme crowding, narrative strength
• new information: orders, customers, products, pricing, policy, competition, capacity, earnings, management guidance

Optional SEC Data Assist

For U.S.-listed companies, use SEC filings as the baseline evidence for reported historical fundamentals. `edgartools` can be used to fetch company filings, XBRL financial statements, filing text, insider transactions, ownership filings, and recent 8-K disclosures.

If the environment does not already have it, install with `pip install edgartools` or `uv pip install edgartools`. The import package is `edgar`, not `edgartools`. SEC access requires an identity; set `EDGAR_IDENTITY="Name email@example.com"` in the environment or call `from edgar import set_identity; set_identity("name@example.com")` before requests.

Minimal usage pattern:

from edgar import Company

company = Company("AAPL")
financials = company.get_financials()
income = financials.income_statement()
balance = financials.balance_sheet()
cashflow = financials.cashflow_statement()

Use SEC data to anchor:

• revenue history, gross margin, operating margin, EPS, free cash flow, capex, debt, cash, dilution, and share-count trends
• segment revenue, customer concentration, backlog/order language, risk-factor changes, and management's stated demand drivers
• 10-K and 10-Q trend baselines for the prior, and 8-K/earnings-release data for the latest update
• Form 4, 13D/G, and 13F data as sentiment/ownership context only, not as intrinsic-growth evidence by itself

Do not use SEC data as a substitute for current market data, consensus estimates, forward multiples, TAM estimates, option activity, or real-time price movement. If using edgartools or SEC filings, name the form and filing date, and separate "reported fact" from "analyst/market estimate."

Growth Hypotheses

Always frame future 3-5 year revenue CAGR as probabilities across these hypotheses:

| Hypothesis | Label | 3-5Y revenue CAGR | | --- | --- | --- | | H0 | contraction | <0% | | H1 | mature slow growth | 0%-5% | | H2 | steady growth | 5%-12% | | H3 | high-cycle growth | 12%-25% | | H4 | structural breakout | 25%-50% | | H5 | platform expansion | >50% |

Workflow

1. Establish The Prior

Assign initial probabilities to H0-H5 using fundamentals, industry cycle, TAM, historical growth, and competitive position.

Prefer a conservative prior when evidence is incomplete. Do not let market excitement alone justify H4 or H5.

2. Classify New Information By Variable

When new information appears, identify which variables it affects:

• revenue growth
• margin
• TAM
• market share
• competitive structure
• cash flow
• valuation multiple
• FOMO sentiment

If information mainly affects market attention, update valuation multiple and FOMO, not intrinsic growth.

3. Bayesian Update

Ask how likely the new information is under each growth hypothesis:

• If the information is more consistent with H3/H4/H5, raise those probabilities.
• If it looks cyclical, one-off, or backlog timing, avoid over-updating long-term growth.
• If it only strengthens narrative or trading enthusiasm, raise FOMO and multiple risk rather than intrinsic growth.
• If it contradicts high growth, shift probability toward H0-H2.

Show the update as prior -> likelihood interpretation -> posterior.

4. Calculate Weighted Intrinsic Growth

Estimate weighted intrinsic 3-5 year revenue CAGR from the posterior probabilities. Use midpoint assumptions unless better evidence is available:

| Hypothesis | Suggested midpoint | | --- | --- | | H0 | -5% | | H1 | 2.5% | | H2 | 8.5% | | H3 | 18.5% | | H4 | 37.5% | | H5 | 60% or scenario-specific |

Report a range, not false precision.

5. Reverse-Engineer Market-Implied Growth

Infer the growth rate embedded in current valuation using market cap or enterpr