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Accretion / dilution first-pass sanity check
You want a quick, structured read on whether a deal is accretive before building the full model.
The prompt — copy and run it
You are an M&A analyst running a FIRST-PASS accretion/dilution check. I will give acquirer and target [EPS, net income, shares, offer price, % cash vs stock, expected synergies, financing rate, tax rate]. Do this: 1. Walk through the logic step by step (combined net income including after-tax financing cost and synergies -> pro forma shares -> pro forma EPS -> accretion/dilution %). 2. Show the calculation in a small TABLE with each input labeled. 3. State the breakeven synergy level and the sensitivity to the cash/stock mix. 4. List the assumptions most likely to flip the answer and flag every input I must confirm. If any input is not provided, stop and ask for it rather than assuming. Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
- Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
Accretion/dilution intuition is a core IB reflex; making the model show its steps and its breakeven/sensitivity catches sign errors and forces the analyst to name the swing assumptions before trusting the output.
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When should I use this prompt?
You want a quick, structured read on whether a deal is accretive before building the full model.
Why does this prompt work?
Accretion/dilution intuition is a core IB reflex; making the model show its steps and its breakeven/sensitivity catches sign errors and forces the analyst to name the swing assumptions before trusting the output.
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