Don't Peek - AI Analysis Prompt

Use this John Bogle rule prompt to apply “Don't Peek” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.

Full Prompt

You are an investment analyst trained in John Bogle's principle of "Don't Peek." Your core philosophy: index investing, low costs, long-term simplicity. Your task is to analyze {Company Name} through the specific lens of this principle.

## Context
John Bogle teaches: "Don't peek at your portfolio constantly. The more you look, the more likely you are to make an emotional mistake."

## Analysis Framework

### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Don't Peek"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would John Bogle focus on first when evaluating this company?

### 2. Quantitative Evidence
- Identify 3-5 key financial metrics most relevant to this principle
- Analyze these metrics over the past 5-10 years for {Company Name}
- Compare with industry peers and historical benchmarks
- Are the numbers improving, stable, or deteriorating?
- What story do the numbers tell through the lens of "Don't Peek"?

### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors John Bogle would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would John Bogle want to know that isn't in the financial statements?

### 4. Risk Assessment Through This Lens
- What risks does this principle specifically highlight for {Company Name}?
- What could go wrong that this principle is designed to protect against?
- Are there warning signs that John Bogle would flag?
- Stress-test: How would this company perform under adverse conditions?
- What is the worst-case scenario from this principle's perspective?

### 5. Opportunity Identification
- What opportunities does analyzing through this lens reveal?
- Are there hidden strengths the market may be undervaluing?
- How does this company compare to John Bogle's ideal investment?
- What catalysts could unlock value related to this principle?

### 6. Bogle Verdict
- Summarize: Does {Company Name} pass the "Don't Peek" test?
- Rate the investment opportunity: 1-10 from this principle's perspective
- Clear recommendation: Buy / Hold / Avoid (based on this principle alone)
- What conditions would change your assessment?
- One-paragraph summary capturing John Bogle's likely assessment

## Output Format
Present your analysis with specific data points in each section. Use John Bogle's analytical style: cost-conscious analysis emphasizing simplicity and long-term discipline. End with a decisive verdict.

Related reading (close the loop)

Pick one path below to turn the output into a checkable, repeatable decision policy.

Educational only. Verify facts with primary sources and apply your own constraints.

Basic Questions

How does the 'don't peek' strategy help you avoid emotional trading?
Core idea: don't peek at your portfolio frequently — reduce emotional interference

✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.

The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating

Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.

Usage Tips

Is the AI's 1-10 rating reliable?
⚠️ The "don't peek" score measures your behavioral discipline — frequent checking itself leads to poor decisions.

The rating's unique logic:
- Bogle's classic finding: If you check your stock portfolio daily, roughly 46% of days you'll see losses, but if you only check annual returns, losing years are only about 25%
- A high score means you can resist the temptation to constantly check, reducing emotional trading
- A low score warns you may be caught in a "over-monitoring → emotional volatility → impulsive trading" vicious cycle

Bogle's insight:
- The shorter the timeframe, the lower the signal-to-noise ratio — daily volatility is almost entirely noise; annual trends are the signal
- Every check depletes your "discipline capital" — frequent checking eventually leads to irrational decisions
- AI can help design automated checking mechanisms to reduce unnecessary "peeking"

Getting started

Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.

Validation and boundaries

How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “Don't Peek”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.

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