pe-buyout

name

pe-buyout

description

PE buyout analysis — LBO modeling, operational value creation, and exit planning. Activate when the user mentions buyout, leveraged buyout, LBO, management buyout, MBO, platform acquisition, bolt-on, add-on, sponsor returns, MOIC, returns attribution, management rollover, sweet equity, ratchet, debt capacity, EBITDA bridge, 100-day plan, or asks about PE acquisition, control investment, or take-private analysis.

PE Buyout Analysis

I analyze control buyout investments from deal screening through exit. Every buyout is a bet on three levers: EBITDA growth, multiple expansion, and deleveraging. I build complete models that make each lever's contribution explicit and stress-testable, with the rigor of a senior associate presenting to investment committee.

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Scope & Boundaries

What this skill DOES:

• Screen businesses for buyout attractiveness (cash flow, defensibility, margin opportunity)
• Build complete LBO models with sources & uses, operating projections, and debt schedule
• Decompose returns into EBITDA growth, multiple expansion/contraction, and deleveraging
• Design operational value creation plans with quantified EBITDA bridges
• Structure management equity programs (rollover, sweet equity, ratchets, options)
• Model platform + bolt-on acquisition strategies with accretion analysis
• Analyze exit scenarios (strategic sale, secondary buyout, IPO, dividend recap)
• Stress test returns against revenue decline, margin compression, and rate increases

Use a different skill when:

• Growth equity / minority investment → /pe-growth
• Private credit / direct lending → /private-credit
• LP secondaries or GP-led continuation → /secondaries
• Sell-side M&A process → /sell-side
• Real estate PE → /re-acquisitions

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Available Tools

Tool	Command	When to Use
LBO	python3 tools/lbo.py	MOIC, IRR, returns attribution
DCF	python3 tools/dcf.py	Standalone valuation for entry price
WACC	python3 tools/wacc.py	Cost of capital, unlevered beta
IRR / NPV	python3 tools/irr.py	General-purpose equity return calculation
Kelly	python3 tools/kelly.py	Position sizing within a PE fund
Credit Spread	python3 tools/credit_spread.py	Z-Score for target companies

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Pre-Flight Checks

1. Target profile: Company name, industry, revenue, EBITDA, margins, growth rate
2. Entry valuation: Enterprise value, EV/EBITDA multiple, equity value
3. Capital structure: Total leverage, debt tranches, rates, amortization
4. Operating assumptions: Revenue growth, margin trajectory, capex, working capital
5. Exit assumptions: Hold period, exit multiple, exit method
6. Value creation thesis: What operational improvements justify the investment?

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Phase 1: Deal Screening

Goal: Assess whether the business is an attractive buyout candidate before modeling.

Buyout attractiveness scorecard:

Criterion	Strong	Moderate	Weak
Revenue visibility	Recurring/contracted	Repeat but discretionary	Project-based/lumpy
Margin opportunity	Below peers, clear levers	In-line, some upside	At/above peers
Cash conversion	FCF/EBITDA >70%	50-70%	<50%
Debt capacity	Stable CF, hard assets	Moderate cyclicality	Highly cyclical
Market position	#1-2 in niche	Top 5	Fragmented, no moat
Management	Strong, aligned	Competent, needs support	Needs replacement
Growth avenues	Organic + M&A	Organic only	Mature/declining

Decision Gate: If the business fails 3+ criteria, it's not a control buyout — consider growth equity or credit instead.

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Phase 2: Sources & Uses and Capital Structure

Goal: Build the transaction capital structure.

SOURCES                         USES
Senior Term Loan    $[X]M       Enterprise Value      $[X]M
Second Lien/Sub     $[X]M       Transaction Fees      $[X]M
Mezzanine           $[X]M       Financing Fees        $[X]M
Rollover Equity     $[X]M       Refinance Existing    $[X]M
Sponsor Equity      $[X]M       Cash to Balance Sheet $[X]M
─────────────────────────       ──────────────────────────────
Total Sources       $[X]M       Total Uses            $[X]M

Leverage benchmarks by industry:

Industry	Total Leverage	Senior	Sub/Mezz
Software/SaaS	5-7x	4-5x	1-2x
Healthcare services	5-6x	3.5-4.5x	1-1.5x
Industrial/manufacturing	3-5x	2.5-3.5x	0.5-1.5x
Consumer/retail	3-4.5x	2.5-3.5x	0.5-1x
Business services	5-6.5x	4-5x	1-1.5x

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Phase 3: LBO Modeling

Goal: Project operating performance and calculate returns.

Run: python3 tools/lbo.py --ebitda [X] --entry-multiple [X] --exit-multiple [X] --leverage [X] --rate [X] --growth [X] --years [X]

Operating Projections (5-Year)

| Metric          | Entry  | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|-----------------|--------|--------|--------|--------|--------|--------|
| Revenue         | $[X]M  |        |        |        |        |        |
| Revenue Growth  |        | [X]%   | [X]%   | [X]%   | [X]%   | [X]%   |
| EBITDA          | $[X]M  |        |        |        |        |        |
| EBITDA Margin   | [X]%   |        |        |        |        |        |
| Capex           |        |        |        |        |        |        |
| Free Cash Flow  |        |        |        |        |        |        |

Returns Attribution

Entry Equity:        $[X]M
Exit Equity:         $[X]M
MOIC:                [X]x
IRR:                 [X]%

Attribution:
  EBITDA Growth:     [X]x  ([X]% of return)
  Multiple Change:   [X]x  ([X]% of return)
  Deleveraging:      [X]x  ([X]% of return)

Sanity check: If >50% of returns come from multiple expansion, the thesis is speculative. If >60% comes from deleveraging, the deal is primarily a leverage play, not a value creation story.

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Phase 4: Operational Value Creation

Goal: Build the EBITDA bridge from entry to exit — the "100-day plan" in numbers.

EBITDA Bridge (Entry → Exit):
  Entry EBITDA:                          $[X]M
  (+) Revenue growth (organic):          $[X]M
  (+) Pricing / mix improvement:         $[X]M
  (+) Cost reduction (COGS):             $[X]M
  (+) SG&A optimization:                 $[X]M
  (+) Procurement savings:               $[X]M
  (+) Bolt-on acquisitions:              $[X]M
  (-) Investment / reinvestment:        ($[X]M)
  = Exit EBITDA:                         $[X]M
  EBITDA CAGR:                           [X]%

Value creation categories:

1. Revenue synergies: Cross-sell, geographic expansion, new products, pricing
2. Cost synergies: Procurement, headcount, facilities, technology consolidation
3. Working capital: Inventory optimization, receivables management, payables extension
4. Capex efficiency: Prioritization, shared services, technology-enabled operations
5. M&A: Bolt-on acquisitions at lower multiples (buy at 5-7x, consolidate onto platform at 10-12x)

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Phase 5: Management Equity & Alignment

Goal: Structure management incentives that align with fund returns.

Management Equity Structure:
  Rollover equity:     [X]% of pre-transaction equity ($[X]M)
  Sweet equity/MIP:    [X]% of post-close equity (vesting over [X] years)
  Options/Ratchet:     Strike at [X]x MOIC, [X]% additional equity at [X]x+

Total management ownership (fully diluted):
  At entry:            [X]%
  At base case exit:   [X]% (after sweet equity + ratchet vest)
  At upside exit:      [X]% (full ratchet)

Alignment test: Does management earn more from operational improvement than from financial engineering? If the majority of management's upside comes from leverage, the incentive structure is misaligned.

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Phase 6: Exit Analysis

Goal: Model multiple exit scenarios with probability-weighted returns.

Exit Scenario	Probability	Exit Multiple	MOIC	IRR
Strategic sale	[X]%	[X]x	[X]x	[X]%
Secondary buyout	[X]%	[X]x	[X]x	[X]%
IPO	[X]%	[X]x	[X]x	[X]%
Dividend recap + hold	[X]%	N/A	[X]x	[X]%
Probability-weighted	100%		[X]x	[X]%

Decision Gate: If the probability-weighted IRR is below the fund's hurdle rate (typically 15-20% net), the deal doesn't clear committee.

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Phase 7: Sensitivity & Stress Testing

IRR Sensitivity: Entry Multiple vs. Exit Multiple

Entry \ Exit	[X-1]x	[X]x	[X+1]x	[X+2]x
[X-1]x
[X]x		base
[X+1]x

Stress Scenarios

Scenario	Assumption	MOIC	IRR	Equity at Risk
Revenue miss -15%	Growth halves	[X]x	[X]%
Margin compression -200bps	Cost inflation	[X]x	[X]%
Multiple contraction -2x	Market correction	[X]x	[X]%
Rate shock +300bps	Floating rate debt	[X]x	[X]%
Combined downside	All of the above	[X]x	[X]%

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Quality Gates

• Buyout attractiveness screened before modeling
• Sources & uses balanced
• Returns decomposed into EBITDA growth / multiple / leverage
• EBITDA bridge from entry to exit with quantified initiatives
• Management equity structure reviewed for alignment
• Multiple exit scenarios with probability-weighted returns
• Sensitivity table and stress tests completed

Hard Constraints

• NEVER present an IRR without returns attribution (growth vs. multiple vs. leverage)
• NEVER assume multiple expansion as the primary return driver without justification
• ALWAYS stress test against revenue miss + margin compression + multiple contraction
• ALWAYS check FCF conversion — EBITDA growth means nothing without cash flow

Common Pitfalls

1. Paying for growth twice — buying at a premium multiple AND modeling aggressive growth
2. Ignoring working capital — fast-growing businesses consume cash in receivables and inventory
3. Straight-line margin expansion — identify specific cost levers, not just "we'll get more efficient"
4. Bolt-on accretion math without integration cost — acquisition synergies take time and money
5. Exit multiple = entry multiple — market conditions change over 5 years

Related Skills

• /pe-growth — minority growth equity investments
• /private-credit — direct lending and subordinate debt
• /secondaries — LP secondaries and GP-led continuation vehicles
• /lbo — focused LBO modeling tool (lighter than full buyout analysis)
• /sell-side — when running the sell-side process for portfolio companies