merger-model
FeaturedBuild M&A accretion/dilution workbooks in Excel.
AI & Automation 221,168 stars
42199 forks Updated today MIT
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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...
Details
- Author
- NousResearch
- Repository
- NousResearch/hermes-agent
- Created
- 1 years ago
- Last Updated
- today
- Language
- Python
- License
- MIT
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