Industry Knowledge - موجّه تحليل بالذكاء الاصطناعي
Use this Peter Lynch rule prompt to apply “معرفة الصناعة” 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.
الموجّه الكامل
أنت محلل استثماري مدرّب على مبدأ Peter Lynch: "Industry Knowledge". مهمتك تحليل {اسم الشركة} من خلال هذا المنظور المحدد.
## السياق
يعلّم Peter Lynch: "If you work in an industry, you have an edge in that industry."
## إطار التحليل
### 1. تقييم تطبيق المبدأ
- كيف ينطبق هذا المبدأ تحديداً على {اسم الشركة}؟
- ما جوانب الشركة الأكثر صلة بـ"Industry Knowledge"؟
- قيّم التوافق: قوي / متوسط / ضعيف
- على ماذا سيركز Peter Lynch أولاً؟
### 2. الأدلة الكمية
- حدد 3-5 مؤشرات مالية رئيسية ذات صلة
- حلل هذه المؤشرات خلال السنوات 5-10 الماضية
- قارن مع المنافسين والمعايير التاريخية
- هل الأرقام تتحسن أم مستقرة أم تتدهور؟
### 3. التحليل النوعي
- قيّم العوامل غير القابلة للقياس التي سيفحصها Peter Lynch
- جودة الإدارة وتوافقها مع هذا المبدأ
- ديناميكيات الصناعة والموقف التنافسي
- استدامة نموذج الأعمال من هذا المنظور
### 4. تقييم المخاطر
- ما المخاطر التي يبرزها هذا المبدأ لـ{اسم الشركة}؟
- ما إشارات التحذير التي سيحددها Peter Lynch؟
- اختبار الضغط: كيف ستؤدي الشركة في ظروف معاكسة؟
- ما أسوأ سيناريو من منظور هذا المبدأ؟
### 5. تحديد الفرص
- ما الفرص التي يكشفها هذا التحليل؟
- هل هناك نقاط قوة مخفية قد يقلل السوق من قيمتها؟
- ما المحفزات التي قد تطلق القيمة؟
### 6. Lynch Verdict
- هل تجتاز {اسم الشركة} اختبار "Industry Knowledge"؟
- التقييم: 1-10
- توصية واضحة: شراء / احتفاظ / تجنب
- ملخص في فقرة واحدة
## تنسيق المخرجات
قدم بيانات محددة في كل قسم. اختم بحكم حاسم.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.
هذا المحتوى متاح حاليًا باللغتين الصينية والإنجليزية فقط.
Basic Questions
How much does industry knowledge help investment decisions? How to build it?
Core idea: deep industry understanding enables good investment decisions
✅ 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.
✅ 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?
⚠️ AI's "industry knowledge depth score" helps judge how significant your knowledge edge really is.
How to interpret:
- **8-10 (deep industry expert)**: Your knowledge far exceeds typical investors — leverage this for concentrated investing
- **5-7 (useful but insufficient depth)**: Some industry knowledge but lacking critical deep insights — need more research
- **1-4 (industry novice)**: Knowledge doesn't exceed publicly available information — don't invest based on assumed industry edge
Lynch said: Invest in what you know. But "knowing" doesn't mean you've heard of the industry — it means you can spot industry changes before Wall Street analysts. AI calibrates your real knowledge level.
How to interpret:
- **8-10 (deep industry expert)**: Your knowledge far exceeds typical investors — leverage this for concentrated investing
- **5-7 (useful but insufficient depth)**: Some industry knowledge but lacking critical deep insights — need more research
- **1-4 (industry novice)**: Knowledge doesn't exceed publicly available information — don't invest based on assumed industry edge
Lynch said: Invest in what you know. But "knowing" doesn't mean you've heard of the industry — it means you can spot industry changes before Wall Street analysts. AI calibrates your real knowledge level.
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 “معرفة الصناعة”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.
المزيد من موجّهات القواعد
استكشف مبادئ استثمارية أخرى من هذا المعلّم.