Intellectual Honesty - Prompt d'Analyse IA
Use this Seth Klarman rule prompt to apply “Honnêteté Intellectuelle” 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.
Prompt Complet
Vous êtes un analyste d'investissement formé au principe de Seth Klarman : « Intellectual Honesty ». Votre tâche est d'analyser {Nom de l'Entreprise} à travers cette perspective spécifique.
## Contexte
Seth Klarman enseigne : « You must be intellectually honest with yourself. Admit when you're wrong. Learn from mistakes. Don't rationalize poor decisions. »
## Cadre d'Analyse
### 1. Évaluation de l'Application du Principe
- Comment ce principe s'applique-t-il spécifiquement à {Nom de l'Entreprise} ?
- Quels aspects de l'entreprise sont les plus pertinents pour « Intellectual Honesty » ?
- Évaluez l'alignement : Fort / Modéré / Faible
- Sur quoi Seth Klarman se concentrerait-il en premier ?
### 2. Preuves Quantitatives
- Identifiez 3-5 métriques financières clés pertinentes
- Analysez ces métriques sur les 5-10 dernières années
- Comparez avec les pairs et les benchmarks historiques
- Les chiffres s'améliorent-ils, sont-ils stables ou se détériorent-ils ?
### 3. Analyse Qualitative
- Évaluez les facteurs non quantifiables que Seth Klarman examinerait
- Qualité de la gestion et alignement avec ce principe
- Dynamique de l'industrie et position concurrentielle
- Durabilité du modèle d'affaires selon cette perspective
### 4. Évaluation des Risques
- Quels risques ce principe met-il en évidence pour {Nom de l'Entreprise} ?
- Quels signaux d'alarme Seth Klarman identifierait-il ?
- Test de résistance : comment l'entreprise performerait-elle en conditions adverses ?
- Quel est le pire scénario selon cette perspective ?
### 5. Identification des Opportunités
- Quelles opportunités cette analyse révèle-t-elle ?
- Y a-t-il des forces cachées sous-évaluées par le marché ?
- Quels catalyseurs pourraient libérer de la valeur ?
### 6. Klarman Verdict
- {Nom de l'Entreprise} passe-t-elle le test de « Intellectual Honesty » ?
- Note : 1-10
- Recommandation claire : Acheter / Conserver / Éviter
- Résumé en un paragraphe
## Format de Sortie
Présentez des données spécifiques dans chaque section. Terminez par un verdict décisif.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.
Ce contenu n'est disponible qu'en chinois et en anglais pour le moment.
Basic Questions
What does Klarman's 'intellectual honesty' look like in practice?
Intellectual honesty is the bedrock of Klarman's investing philosophy:
🪞 Practicing intellectual honesty:
1. Acknowledge uncertainty: Never say 'guaranteed profit,' say 'I believe probability is higher'
2. Distinguish facts from opinions: What you know is fact; what you judge is opinion
3. Record decision reasons: Write down why you buy, for post-verification
4. Welcome contrary evidence: Actively seek information proving you wrong
❌ Signs of dishonesty:
- Only reading analysis supporting your view
- Making excuses after losses instead of finding causes
- Boasting about successes while hiding failures
🪞 Practicing intellectual honesty:
1. Acknowledge uncertainty: Never say 'guaranteed profit,' say 'I believe probability is higher'
2. Distinguish facts from opinions: What you know is fact; what you judge is opinion
3. Record decision reasons: Write down why you buy, for post-verification
4. Welcome contrary evidence: Actively seek information proving you wrong
❌ Signs of dishonesty:
- Only reading analysis supporting your view
- Making excuses after losses instead of finding causes
- Boasting about successes while hiding failures
Usage Tips
Is the AI's 1-10 rating reliable?
⚠️ The intellectual honesty score is a "mirror" — it reflects not whether the company is good, but whether your analytical process is honest.
The rating's unique perspective:
- Klarman believes: The biggest enemy in investing isn't the market but your own biases and self-deception
- A high score means your analysis fully considers the possibility that you could be wrong — this is real risk management
- A low score reveals you may be unconsciously seeking only evidence that supports your conclusion
The most critical reminder:
- "Confirmation bias" is the most common and deadly bias among investors — the more certain you are, the more dangerous
- AI itself can exhibit "confirmation bias" — it may tend to provide analysis you want to hear
- The ultimate test of intellectual honesty: Are you truly willing to accept that your analysis could be completely wrong?
The rating's unique perspective:
- Klarman believes: The biggest enemy in investing isn't the market but your own biases and self-deception
- A high score means your analysis fully considers the possibility that you could be wrong — this is real risk management
- A low score reveals you may be unconsciously seeking only evidence that supports your conclusion
The most critical reminder:
- "Confirmation bias" is the most common and deadly bias among investors — the more certain you are, the more dangerous
- AI itself can exhibit "confirmation bias" — it may tend to provide analysis you want to hear
- The ultimate test of intellectual honesty: Are you truly willing to accept that your analysis could be completely wrong?
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 “Honnêteté Intellectuelle”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.
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