Bottom-Up Analysis - AI Analysis Prompt
Use this Seth Klarman rule prompt to apply “Bottom-Up Analysis” 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 Seth Klarman's principle of "Bottom-Up Analysis." Your core philosophy: margin of safety, patience, catalyst-driven value. Your task is to analyze {Company Name} through the specific lens of this principle.
## Context
Seth Klarman teaches: "We are bottom-up investors. We don't make macro predictions - we find individual securities that are mispriced."
## Analysis Framework
### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Bottom-Up Analysis"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would Seth Klarman 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 "Bottom-Up Analysis"?
### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors Seth Klarman would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would Seth Klarman 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 Seth Klarman 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 Seth Klarman's ideal investment?
- What catalysts could unlock value related to this principle?
### 6. Klarman Verdict
- Summarize: Does {Company Name} pass the "Bottom-Up Analysis" 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 Seth Klarman's likely assessment
## Output Format
Present your analysis with specific data points in each section. Use Seth Klarman's analytical style: deep value analysis seeking catalysts with significant margin of safety. End with a decisive verdict.Related reading (close the loop)
Pick one path below to turn the output into a checkable, repeatable decision policy.
- Read the matching principleDefinition, boundaries, pitfalls, and a minimal checklist.
- Master profileMethodology summary + common misreads for this framework.
- Practice in scenariosTranslate conclusions into “what I do under stress”.
- More prompts from this masterTriangulate with multiple rules instead of anchoring on one prompt.
Educational only. Verify facts with primary sources and apply your own constraints.
Basic Questions
Why is bottom-up analysis better suited for individual investors than macro forecasting?
Klarman insists on bottom-up analysis because:
📌 Problems with macro forecasting:
1. Extremely low accuracy — even central banks and economists frequently err
2. Even if direction is right, timing is hard
3. Correct macro doesn't mean correct stock picks
✅ Bottom-up advantages:
1. You can study every detail of a single company
2. Company financial data is verifiable (unlike lagging macro indicators)
3. Finding undervalued good companies profits regardless of macro environment
Klarman's method: Find good companies first, then check if macro poses additional risk.
📌 Problems with macro forecasting:
1. Extremely low accuracy — even central banks and economists frequently err
2. Even if direction is right, timing is hard
3. Correct macro doesn't mean correct stock picks
✅ Bottom-up advantages:
1. You can study every detail of a single company
2. Company financial data is verifiable (unlike lagging macro indicators)
3. Finding undervalued good companies profits regardless of macro environment
Klarman's method: Find good companies first, then check if macro poses additional risk.
Usage Tips
Is the AI's 1-10 rating reliable?
⚠️ The bottom-up score measures "your depth of understanding of this specific company," not macro trend judgment.
The rating's unique logic:
- Klarman believes most investors err because they decide top-down — looking at macro first then finding targets, rather than starting from the specific company
- A high score means your analysis focuses on the company's intrinsic value, undistracted by macro noise
- A low score suggests your analysis may over-rely on macro judgments or industry trends while neglecting company specifics
Usage notes:
- Bottom-up doesn't mean "ignore macro" but rather "look at the company first, then consider how macro affects the company"
- AI may unconsciously inject too much macro narrative — insist it focus on company-specific data
- The best bottom-up analysis answers: "What is this company worth at a minimum?" (liquidation value thinking)
The rating's unique logic:
- Klarman believes most investors err because they decide top-down — looking at macro first then finding targets, rather than starting from the specific company
- A high score means your analysis focuses on the company's intrinsic value, undistracted by macro noise
- A low score suggests your analysis may over-rely on macro judgments or industry trends while neglecting company specifics
Usage notes:
- Bottom-up doesn't mean "ignore macro" but rather "look at the company first, then consider how macro affects the company"
- AI may unconsciously inject too much macro narrative — insist it focus on company-specific data
- The best bottom-up analysis answers: "What is this company worth at a minimum?" (liquidation value thinking)
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 “Bottom-Up Analysis”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.
More Rule Prompts
Explore other investment principles from this master.
Intellectual Honesty
You must be intellectually honest with yourself. Admit when you're wrong. Learn from mistakes. Don't rationalize poor decisions.
→Patience
Patience is an essential virtue for value investors. The market will eventually recognize value, but the timing is uncertain.
→Complex Situations
We seek opportunity in complexity - spinoffs, restructurings, bankruptcies. Where others see chaos, we see potential value.
→Catalyst-Driven Investing
We prefer investments where a catalyst exists to unlock value. Time is money - we want to know why and when value will be realized.
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