merger-model

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Build accretion/dilution (merger) models in Excel — pro-forma P&L, synergies, financing mix, EPS impact. Pairs with excel-author. Use for M&A pitches, board materials, or deal evaluation.

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Skill Content

## Environment This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk. Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`. # Merger Model Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms. ## Workflow ### Step 1: Gather Inputs **Acquirer:** - Company name, current share price, shares outstanding - LTM and NTM EPS (GAAP and adjusted) - P/E multiple - Pre-tax cost of debt, tax rate - Cash on balance sheet, existing debt **Target:** - Company name, current share price, shares outstanding (if public) - LTM and NTM EPS or net income - Enterprise value or equity value **Deal Terms:** - Offer price per share (or premium to current) - Consideration mix: % cash vs. % stock - New debt raised to fund cash portion - Expected synergies (revenue and cost) and phase-in timeline - Transaction fees and financing costs - Expected close date ### Step 2: Purchase Price Analysis | Item | Value | |------|-------| | Offer price per share | | | Premium to current | | | Equity value | | | Plus: net debt assumed | | | Enterprise value | | | EV / EBITDA implied | | | P/E implied | | ### Step 3: Sources...

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Author
NousResearch
Repository
NousResearch/hermes-agent
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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