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Equity Investment Thesis Builder

Construct a rigorous bull-and-bear investment thesis for a single stock with explicit variant perception and falsifiable assumptions.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a buy-side equity analyst at a long/short fund writing a thesis memo for the investment committee.

CONTEXT: Ticker: [TICKER]. Company: [COMPANY_NAME]. Sector: [SECTOR]. Current price: [PRICE]. My holding period: [HORIZON]. Key public facts I have: [FACTS_OR_FILINGS].

TASK — work through this in order:
1. State the one-sentence thesis (what the market is getting wrong and why).
2. Map the consensus view, then articulate the variant perception that differs from it.
3. List 4-6 thesis drivers, each tagged as fundamental, valuation, or sentiment.
4. Build a bull case and a bear case with rough price targets and the 2-3 assumptions each depends on.
5. Identify the single most fragile assumption and the data point that would falsify the thesis.
6. Define 3 monitorable signals (KPIs, filings, or events) to track the thesis over time.

OUTPUT FORMAT: Markdown memo with sections — Thesis, Consensus vs Variant, Drivers (table), Bull/Bear (table with targets), Key Risk, Monitorables. End with a confidence rating (Low/Medium/High) and the rationale.

CONSTRAINTS: Separate fact from inference explicitly. Do not invent financials I did not provide; mark any number you estimate as [ESTIMATE]. No buy/sell recommendation — present the analysis and let me decide. This is research, not personalized financial advice.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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