Reflexivity Theory - AI Analysis Prompt
Use this George Soros rule prompt to apply “Reflexivity Theory” 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 George Soros's principle of "Reflexivity Theory." Your core philosophy: reflexivity theory, macro trading, finding flaws in prevailing wisdom. Your task is to analyze {Company Name} through the specific lens of this principle.
## Context
George Soros teaches: "Markets are not efficient; they are reflexive. Participant perceptions and market fundamentals influence each other in a circular feedback loop, creating trends that can become self-reinforcing until they inevitably reverse."
## Analysis Framework
### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Reflexivity Theory"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would George Soros 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 "Reflexivity Theory"?
### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors George Soros would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would George Soros 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 George Soros 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 George Soros's ideal investment?
- What catalysts could unlock value related to this principle?
### 6. Soros Verdict
- Summarize: Does {Company Name} pass the "Reflexivity Theory" 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 George Soros's likely assessment
## Output Format
Present your analysis with specific data points in each section. Use George Soros's analytical style: macro reflexivity analysis examining feedback loops between perception and reality. 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
What is reflexivity theory and how does it explain bubbles and crashes?
Reflexivity is Soros's most fundamental theoretical contribution:
🔄 Reflexivity loop:
1. Perception affects reality: Investors favor a stock → buying pushes price up
2. Reality affects perception: Price rises → more people think it's good → more buying
3. Self-reinforcement: Positive feedback loop, price detaches from fundamentals
4. Eventual reversal: When reality can't support perception, bubble bursts
📊 Classic examples:
- 2000 dot-com bubble: Optimism → buying → price up → more optimism → bubble
- 2008 mortgage crisis: Reverse cycle — panic → selling → price down → more panic → crash
🔄 Reflexivity loop:
1. Perception affects reality: Investors favor a stock → buying pushes price up
2. Reality affects perception: Price rises → more people think it's good → more buying
3. Self-reinforcement: Positive feedback loop, price detaches from fundamentals
4. Eventual reversal: When reality can't support perception, bubble bursts
📊 Classic examples:
- 2000 dot-com bubble: Optimism → buying → price up → more optimism → bubble
- 2008 mortgage crisis: Reverse cycle — panic → selling → price down → more panic → crash
Usage Tips
Is the AI's 1-10 rating reliable?
⚠️ The rating needs dynamic interpretation within the reflexivity framework.
The rating's value from a reflexivity perspective:
- Represents a static snapshot at the current moment, but reflexivity emphasizes markets are dynamic loops
- A high score may indicate we're in a positive feedback loop — but this could mean a bubble is inflating
- A low score may mean a negative feedback loop is underway — but this could signal a reversal opportunity approaching
Key limitations:
- AI struggles to identify inflection points in reflexive cycles — yet these are the most critical trading moments
- Self-reinforcing market sentiment effects are difficult to quantify in AI models
- The same company can receive very different scores at different reflexive stages
✅ Right approach: View the rating as a snapshot within the reflexive cycle, and focus more on trend direction and cycle stage.
The rating's value from a reflexivity perspective:
- Represents a static snapshot at the current moment, but reflexivity emphasizes markets are dynamic loops
- A high score may indicate we're in a positive feedback loop — but this could mean a bubble is inflating
- A low score may mean a negative feedback loop is underway — but this could signal a reversal opportunity approaching
Key limitations:
- AI struggles to identify inflection points in reflexive cycles — yet these are the most critical trading moments
- Self-reinforcing market sentiment effects are difficult to quantify in AI models
- The same company can receive very different scores at different reflexive stages
✅ Right approach: View the rating as a snapshot within the reflexive cycle, and focus more on trend direction and cycle stage.
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 “Reflexivity Theory”, 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.
Find the Flaw
The prevailing wisdom is always wrong. Find the flaw in the prevailing bias and bet against it when conditions change. The bigger the flaw in conventional thinking, the bigger the opportunity.
→Bet Big When Right
It's not whether you're right or wrong that's important, but how much money you make when you're right and how much you lose when you're wrong. When you have conviction, bet big.
→Survive First
My approach works not by making valid predictions but by allowing me to correct false ones. I am only rich because I know when I am wrong. Play to survive first, then to make money.
→Test Your Hypothesis
Start with a hypothesis about market behavior, then test it with a small position. If the market confirms your hypothesis, add to your position. If it contradicts you, cut quickly and reassess.
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