M&A Accretion and Dilution Model

Model architecture

• Define purchase consideration.
• Model sources and uses.
• Estimate purchase accounting adjustments.
• Combine earnings and synergies.
• Calculate pro forma EPS and leverage.

A professional model should make the decision logic visible. Inputs belong in a controlled assumption area; calculations should be formula-driven; outputs should state units, dates and scenarios; checks should be obvious and actionable.

Core inputs

• Offer price and shares
• Financing mix
• Target financials
• Synergies and implementation costs
• Tax and amortisation

Formula logic

Use the formulas as design relationships, not as substitutes for the accounting policy, contract definition or transaction facts relevant to the model.

Practical example

A buyer funds half the consideration with debt and half with shares. Interest expense, new shares, synergy timing and intangible amortisation determine whether the deal is accretive.

How to implement

1. Load the historical base and reconcile it.
2. Put assumptions in dedicated cells.
3. Build the schedule from operational drivers.
4. Link outputs to financial statements and dashboards.
5. Run base, upside and downside checks.

Control checks

• Sources equal uses
• Purchase consideration reconciles
• Synergy ramp and one-time costs are separated
• Share issuance and debt interest are linked
• Goodwill bridge closes

Common modeling errors

• Counting full run-rate synergies in year one
• Ignoring transaction fees
• Using book value instead of fair value adjustments
• Confusing accretion with value creation
• Missing tax effects

Practical Q&A

Should the model contain all possible detail?

No. It should contain enough detail to answer the decision question and explain material risks. Excess detail can hide the drivers.

Should a formula ever contain a hardcoded number?

Only for constants that are genuinely universal or immaterial. Business assumptions should be linked to visible input cells.

What is the minimum review standard?

Reconcile historical data, test key formulas independently, scan for hardcodes and errors, verify scenario switches, and review outputs under downside assumptions.