Library/Account Research & Buyer Intelligence/Financial & SEC Filing Research/Evaluate M&A Valuation Methods for a Target
TestedAccount Research & Buyer IntelligenceQuick Win (under 10 min)
About this prompt
This prompt evaluates multiple M&A valuation methods — DCF, comparable company analysis, precedent transaction analysis, and asset-based approaches — for a specific target, comparing the implied valuations and the strategic premium drivers. Use it when building an M&A valuation package that needs to support a board or investment committee decision.Single-method valuations are easy to build and easy to attack — this prompt builds the multi-method approach that withstands scrutiny. Provide the [target company's financials], [comparable companies], and [recent relevant transactions] and get an analysis covering: DCF valuation with the key assumptions and their sensitivity, comparable company trading multiples with a discount/premium analysis, precedent transaction multiples from relevant deals, the range of implied valuations across methods, the strategic premium drivers that justify paying above the base valuation, and a recommended valuation range for negotiation. Use this over a general financial analysis prompt when you're preparing for an M&A transaction and need a defensible valuation that can be presented to a board or investors. Tip: the sensitivity analysis on the DCF assumptions is the section most scrutinized in investment committee reviews — the assumptions that produce the widest range of outcomes under small changes are the ones to stress-test most thoroughly before presenting.The prompt
Evaluate various valuation methods and techniques for mergers and acquisitions and discuss their suitability for [Company Name]'s M&A strategy.
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Variables to replace
COMPANY NAME
How to run it
- Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
- Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
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What good output looks like
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