merger-model

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 & Uses

Sources	$	Uses	$
New debt		Equity purchase price
Cash on hand		Refinance target debt
New equity issued		Transaction fees
		Financing fees
Total		Total

Step 4: Pro Forma EPS (Accretion / Dilution)

Calculate year-by-year (Year 1-3):

	Standalone	Pro Forma	Accretion/(Dilution)
Acquirer net income
Target net income
Synergies (after tax)
Foregone interest on cash (after tax)
New debt interest (after tax)
Intangible amortization (after tax)
Pro forma net income
Pro forma shares
Pro forma EPS
Accretion / (Dilution) %

Step 5: Sensitivity Analysis

Accretion/Dilution vs. Synergies and Offer Premium:

	$0M syn	$25M syn	$50M syn	$75M syn	$100M syn
15% premium
20% premium
25% premium
30% premium

Accretion/Dilution vs. Cash/Stock Mix:

	100% cash	75/25	50/50	25/75	100% stock
Year 1
Year 2

Step 6: Breakeven Synergies

Calculate the minimum synergies needed for the deal to be EPS-neutral in Year 1.

Step 7: Output

• Excel workbook with:
  • Assumptions tab
  • Sources & uses
  • Pro forma income statement
  • Accretion/dilution summary
  • Sensitivity tables
  • Breakeven analysis
• One-page merger consequences summary for pitch book

Important Notes

• Always show both GAAP and adjusted (cash) EPS where relevant
• Stock deals: use acquirer's current price for exchange ratio, note dilution from new shares
• Include purchase price allocation — goodwill and intangible amortization matter for GAAP EPS
• Synergy phase-in is critical — Year 1 is often only 25-50% of run-rate synergies
• Don't forget foregone interest income on cash used and new interest expense on debt raised
• Tax rate on synergies and interest adjustments should match the acquirer's marginal rate

Data sources — MCP first, web fallback

Many passages below say "use the S&P Kensho MCP / Daloopa MCP / FactSet MCP". Those are commercial financial-data MCPs from the original Cowork plugin context. In Hermes:

• If you have any structured financial-data MCP configured (Hermes supports MCP — see native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.
• Otherwise, fall back to:
  • web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filings
  • Company IR pages for press releases, earnings decks
  • browser_navigate for interactive data portals
  • User-provided data (explicitly ask when the context doesn't have it)
• Never fabricate. If a multiple, precedent, or filing number can't be sourced, flag the cell as [UNSOURCED] and surface it to the user.

Attribution

This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the excel-author skill's conventions. Original: https://github.com/anthropics/financial-services