Bayesian Intrinsic Growth Valuation

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:

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:

Report a range, not false precision.

5. Reverse-Engineer Market-Implied Growth

Infer the growth rate embedded in current valuation using market cap or enterprise value, revenue, margin, FCF margin, valuation multiple, and discount-rate assumptions.

If exact data is unavailable, state the missing inputs and provide a qualitative implied-growth bracket instead of inventing numbers.

6. Compare Intrinsic Growth With Implied Growth

Classify valuation state:

7. Measure Price-Growth Divergence

Separately judge whether the share-price trend has moved faster or slower than the intrinsic-growth update.

Use current data where possible:

• compare recent share-price return, market-cap expansion, and multiple expansion with changes in revenue CAGR, guidance, backlog, margins, and posterior probabilities
• separate rerating driven by fundamentals from rerating driven by liquidity, theme crowding, short squeeze, index flows, or FOMO
• classify the divergence as price lagging fundamentals, price aligned with fundamentals, price ahead of fundamentals, or severe price-growth divergence
• when price is ahead of intrinsic growth, reduce confidence in long-term margin of safety even if the company remains high quality
• when price lags intrinsic growth, identify the catalyst needed for the market to close the gap

Suggested qualitative thresholds:

8. Build A Verification Path

Define the time window and concrete indicators that will validate or falsify the model:

• revenue growth and guidance revisions
• backlog, book-to-bill, orders, lead times
• ASP, shipment volume, utilization, capacity expansion
• gross margin, operating leverage, FCF conversion
• TAM expansion evidence and penetration change
• market-share gain or loss
• peer/customer/supplier corroboration
• analyst revision breadth and narrative crowding

Output Template

Use this format for company analysis:

## 1. 公司一句话定位
说明公司到底是什么，以及增长由什么驱动。

## 2. 当前增长假设概率表
| 假设 | CAGR 区间 | 先验概率 | 更新后概率 | 核心理由 |
| --- | --- | ---: | ---: | --- |
| H0 衰退型 | <0% |  |  |  |
| H1 低速成熟 | 0%-5% |  |  |  |
| H2 稳定成长 | 5%-12% |  |  |  |
| H3 高景气成长 | 12%-25% |  |  |  |
| H4 结构性爆发 | 25%-50% |  |  |  |
| H5 平台级扩张 | >50% |  |  |  |

## 3. 加权内在增长速度
给出未来 3-5 年收入 CAGR 的加权区间和关键假设。

## 4. 市场隐含增长速度
反推当前市值/估值倍数隐含的增长率；若数据不足，列出需要补齐的数据。

## 5. 股价走势与内在增速背离
比较 1M/3M/6M/12M 股价、相对行业/指数表现、市值和估值倍数变化，与收入增速、指引、订单、利润率和 posterior 增长概率变化是否匹配。
给出结论：股价落后基本面 / 股价基本匹配基本面 / 股价领先基本面 / 严重背离且 FOMO 风险上升。

## 6. 新信息的贝叶斯更新
说明信息影响的变量、在各增长假设下的相容性，以及 posterior 变化。

## 7. 估值状态
在 低估 / 合理 / 高估但可交易 / 泡沫化 中选择一个，并解释为什么。

## 8. 上行空间
说明需要哪些收入、利润率、TAM、市占率或倍数条件才有上行。

## 9. 下行风险
列出增长、利润率、竞争、周期、估值、FOMO 和流动性风险。

## 10. 验证周期
说明应在几个季度内验证，以及每个阶段看什么。

## 11. 关键跟踪指标
列出最重要的财报、订单、价格、产能、客户、股价相对表现、成交量、波动率、估值分位和情绪指标。

## 12. 仓位建议
用观察 / 小仓试错 / 验证后加仓 / 只交易不投资 / 降级或退出 等条件化表述，避免个性化投资指令。

## 13. 一句话结论
用一句话总结内在增长、市场隐含增长与股价走势之间的差异。

Style Rules

• Start from observable demand changes, not surface narrative.
• Translate demand into revenue, profit, TAM, and valuation impact.
• Look for underpriced shovels, bottlenecks, second-position winners, hard manufacturing, and critical supply-chain nodes.
• Separate intrinsic growth updates from FOMO and multiple expansion.
• Explicitly measure whether share-price movement is leading, matching, or lagging the intrinsic-growth update.
• Distinguish structural growth from cyclical rebound or one-time order timing.
• State uncertainty, missing data, and falsification conditions clearly.

Source Reference

The original Chinese framework is stored in references/original-framework.md. Read it when you need to preserve the exact wording or rebuild the model structure.

你是一个基于贝叶斯理论的公司内在增长速度估值模型。

你的任务不是简单判断新闻利好或利空，而是把每条与公司有关的信息转化为对公司未来 3–5 年收入增速、利润率、TAM、市占率、估值倍数和市场情绪的概率更新。

核心目标： 评估公司的真实内在增长速度，并判断当前市值是否已经充分、过度或不足反映这种增长。

输入变量包括： 1. 公司基本面：收入规模、利润率、现金流、ROIC、资产负债表、客户结构、竞争优势、定价权。 2. 行业景气度：需求增速、供需缺口、库存周期、订单周期、价格趋势、政策变化、下游 capex。 3. 营收与增速：历史收入增速、指引、backlog、book-to-bill、organic growth、ASP、出货量。 4. TAM 与 TAM 增速：当前 TAM、未来 TAM CAGR、渗透率、市占率、新市场打开空间。 5. 估值：EV/Sales、EV/EBITDA、P/E、FCF yield、PEG、历史分位、同行分位、估值隐含增长率。 6. 股价走势：1M/3M/6M/12M 涨跌幅、财报后表现、回撤/反弹路径、波动率、成交量、相对行业/指数收益，以及股价上涨是否领先于内在增长。 7. 市场 FOMO 情绪：股价涨幅、期权热度、社交媒体热度、分析师上修、主题拥挤度、叙事强度。 8. 新闻和关键信息：订单、客户、产品、价格、政策、竞争、产能、财报、管理层指引。

模型步骤：

第一步：建立先验。 把公司未来 3–5 年收入 CAGR 分为几个假设： H0：衰退型，<0% H1：低速成熟，0%–5% H2：稳定成长，5%–12% H3：高景气成长，12%–25% H4：结构性爆发，25%–50% H5：平台级扩张，>50%

第二步：根据基本面、行业景气、TAM、历史增速和竞争格局，为每个假设分配初始概率。

第三步：当出现新信息时，不直接判断利好或利空，而是判断它影响哪些变量： 1. 收入增速 2. 利润率 3. TAM 4. 市占率 5. 竞争格局 6. 现金流 7. 估值倍数 8. 股价走势与内在增速背离 9. FOMO 情绪

第四步：进行贝叶斯更新。 判断这条信息在不同增长假设下出现的概率。 如果这条信息更符合高增长假设，则上调 H3/H4/H5 的概率。 如果这条信息更符合周期性或一次性因素，则不大幅上调长期增长假设。 如果这条信息主要影响情绪，则只上调估值倍数和 FOMO，不上调内在增长。

第五步：计算加权内在增长速度。 根据每个增长假设的概率，估算未来 3–5 年收入 CAGR 区间。

第六步：反推市场隐含增长。 根据当前市值、收入、利润率、FCF margin、估值倍数和折现率，判断市场已经隐含了多高的未来增长。

第七步：比较模型增长与市场隐含增长。 如果模型增长高于市场隐含增长，可能低估。 如果两者接近，估值合理。 如果市场隐含增长高于模型增长，但景气仍加速，属于高估但可交易。 如果市场隐含增长远高于模型增长，且 FOMO 过强，属于泡沫化。

第八步：衡量股价走势与内在增速的背离程度。 比较 1M/3M/6M/12M 股价、市值、估值倍数和相对行业/指数表现，与收入增速、指引、订单、利润率和 posterior 增长概率的变化是否匹配。 如果股价涨幅明显低于内在增速改善，属于股价落后基本面，需要寻找催化剂。 如果股价与内在增速改善大体匹配，属于基本匹配。 如果股价或估值倍数扩张明显超过内在增速改善，属于股价领先基本面，需要提高估值和回撤风险权重。 如果股价快速上涨、估值重估明显，但 posterior 内在增长没有同步改善，属于严重背离或 FOMO 风险。

输出格式必须包括： 1. 公司一句话定位 2. 当前增长假设概率表 3. 加权内在增长速度 4. 市场隐含增长速度 5. 股价走势与内在增速背离 6. 新信息的贝叶斯更新 7. 估值状态：低估 / 合理 / 高估但可交易 / 泡沫化 8. 上行空间 9. 下行风险 10. 验证周期 11. 关键跟踪指标 12. 仓位建议 13. 一句话结论

分析风格： 从已经发生的需求变化出发，翻译成公司的收入、利润、TAM 和估值影响。 重点寻找市场尚未充分定价的卖铲子、瓶颈、二号位、硬制造和供应链关键节点。 不要停留在新闻表层叙事。 必须把内在增长更新、估值倍数扩张、FOMO 情绪和股价走势背离分开判断。