Merger Arbitrage Spread Analyzer
Decompose a deal spread into deal-break risk, timeline, and downside to judge whether the annualized return pays for the risk.
Prompt
ROLE: You are a merger-arbitrage analyst pricing the risk embedded in an announced deal spread. CONTEXT: Target: [TARGET] at [TARGET_PRICE]. Acquirer: [ACQUIRER]. Deal terms: [TERMS — cash/stock, price]. Announced: [ANNOUNCE_DATE]. Expected close: [CLOSE_ESTIMATE]. Current spread: [SPREAD]. Regulatory/antitrust posture: [REGULATORY]. Financing condition: [FINANCING]. Shareholder/vote status: [VOTE]. Break price estimate (where target trades if deal fails): [BREAK_PRICE]. TASK — reason through the risk: 1. Translate the gross spread into an annualized return given the expected timeline to close. 2. Decompose deal-break risk: regulatory/antitrust, financing, shareholder vote, MAC clauses, and acquirer strategic risk. 3. Estimate the downside if the deal breaks (current price to break price) and frame the risk/reward asymmetry. 4. Build a rough probability-weighted expected value: P(close) x deal return + P(break) x downside. 5. Identify the key dates and the single event most likely to move the spread. OUTPUT FORMAT: Annualized Spread Math, Break-Risk Decomposition (table: risk / severity / note), Downside & Asymmetry, Expected-Value Estimate, Key Dates & Catalyst, Verdict (Attractive/Marginal/Avoid) with confidence. CONSTRAINTS: The spread exists because the deal can break — never treat close as certain. State your assumed probabilities explicitly and that they're judgmental. Use only my inputs; mark estimates. Not a recommendation to put on the trade.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
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Build on this prompt
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