> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mibyanai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Merger Model — Build M&A accretion/dilution workbooks in Excel

> Build M&A accretion/dilution workbooks in Excel

Build M\&A accretion/dilution workbooks in Excel.

## Skill metadata

| | |
| - | - |
| Source | Optional — install with `mibyan skills install official/finance/merger-model` |
| Path | `optional-skills/finance/merger-model` |
| Version | `1.0.0` |
| Author | Anthropic (adapted by Nous Research) |
| License | Apache-2.0 |
| Platforms | linux, macos, windows |
| Tags | `finance`, `m-and-a`, `merger`, `accretion-dilution`, `excel`, `openpyxl`, `modeling`, `investment-banking` |
| Related skills | [`excel-author`](/desktop/user-guide/skills/optional/finance/finance-excel-author), [`pptx-author`](/desktop/user-guide/skills/optional/finance/finance-pptx-author), [`dcf-model`](/desktop/user-guide/skills/optional/finance/finance-dcf-model), [`3-statement-model`](/desktop/user-guide/skills/optional/finance/finance-3-statement-model) |

## Reference: full SKILL.md

<Info>
  The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
</Info>

## 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 Mibyan:

* **If you have any structured financial-data MCP configured** (Mibyan 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](https://github.com/anthropics/financial-services)


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