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M&A deal modeling: accretion/dilution analysis, LBO models, DCF valuation, comparable company analysis, precedent transactions, and synergy estimation. Full three-statement merger models.
Passed 7 of 21 test cases with the skill, and 14 of 21 without it.
and 1 more in the full report
2 fixed18 passed either way0 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
When consolidating two public companies in an M&A model, why can't we simply add historical line items of both balance sheets together without adjustments?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies the need for purchase accounting adjustments including asset write-ups and goodwill creation | Passed | Passed |
| Notes that debt refinancing and new intangibles amortization alter pro forma figures | Missed | Passed |
The instructions your model reads when the skill loads.
A merger model estimates the financial impact of an acquisition or merger: purchase price, financing mix, pro forma statements, synergies, accretion/dilution, leverage, and returns. Use it for M&A analysis, not just valuation in isolation.
Use this skill when:
A proper merger model usually includes:
def accretion_dilution(acquirer_net_income, target_net_income,
acquirer_shares, new_shares_issued,
after_tax_synergies=0.0):
pro_forma_income = acquirer_net_income + target_net_income + after_tax_synergies
pro_forma_shares = acquirer_shares + new_shares_issued
base_eps = acquirer_net_income / acquirer_shares
pro_forma_eps = pro_forma_income / pro_forma_shares
accretion_pct = (pro_forma_eps / base_eps - 1) * 100
return {
'base_eps': round(base_eps, 4),
'pro_forma_eps': round(pro_forma_eps, 4),
test cases passed, vs 14 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.