Acquisition Modeling 101 for Buyers: LBO Math, Sources & Uses, Returns Waterfall (2026)

Acquisition modeling — the LBO model — is the buyer’s underwriting backbone for any private equity-style transaction. It translates a target business’s historical financials into projected returns under various scenarios, accounting for the deal’s capital structure, operational thesis, and exit assumptions. For LMM acquisitions in 2026, modeling discipline separates buyers who close at sustainable returns from those who chase deals into bad outcomes. The model isn’t optional; it’s the underwriting tool that reveals whether the deal economics work.

This guide is the working playbook for acquisition modeling. We’ll walk through the model’s core structure: sources & uses (cash, debt, seller note, equity), P&L projections (revenue, margin, capex, working capital), debt schedule (amortization, interest, paydown), equity returns waterfall (IRR, MOIC across scenarios), and sensitivity analysis (multiple expansion, EBITDA growth, exit multiple). Plus the common modeling errors that destroy returns and credibility. The goal: a buyer reading this can build a credible LMM acquisition model that withstands lender, LP, and investment committee scrutiny.

The framework comes from working alongside 76+ active U.S. lower middle-market buyers including PE platforms running multiple parallel acquisition models, family offices evaluating direct deals, search funders building first-time models, and independent sponsors raising LP capital based on model outputs. We’re a buy-side partner. The buyers pay us when a deal closes — not the seller. The patterns below come from observed model structures across hundreds of LMM acquisitions in the $5M-$300M EV range. Most buyers use Excel; some use specialized tools (eFront, Pitchbook, MackeyRMS, Capital IQ); the underlying logic is consistent regardless of platform.

One framing note before we start. An acquisition model is a tool, not an answer. The model’s job is to reveal what assumptions need to be true for the deal to deliver target returns — and to test what happens when those assumptions break. Buyers who treat the model as an answer (‘the model says 24% IRR, so let’s do the deal’) often miss the underlying assumptions driving that output. Buyers who treat the model as a tool (‘the model tells me I’m betting on 12% revenue growth and 200bps margin expansion; do I believe that?’) make better decisions. The framework below emphasizes the latter approach.

“The mistake new buyers make in their first 5 acquisition models is treating the model as a calculator instead of an underwriting tool. The model isn’t supposed to tell you whether to do the deal — it’s supposed to reveal what the deal needs to be true for it to work, and which assumptions you’re betting on. Models that produce 28% IRR on every reasonable case are signaling deal momentum, not deal quality. The most useful models are the ones that show how quickly returns deteriorate when reasonable assumptions break.”

Key Takeaways

• Acquisition model translates target financials into projected returns; covers sources & uses, P&L projections, debt schedule, returns waterfall, sensitivity analysis.
• Standard 2026 LMM capital structure: 50% senior/unitranche debt + 30% buyer equity + 20% seller note + close-out cash.
• P&L projections: revenue assumptions (organic + price/volume + add-ons), margin trajectory, capex (2-5% of revenue), working capital build.
• Returns waterfall: IRR (annualized), MOIC (cash multiple). PE target 20%+ IRR / 3x+ MOIC base case; search funders 25%+ / 4x+.
• Sensitivity matrix: typically 5×5 grid showing IRR/MOIC across exit multiple (4x-7x) and EBITDA growth (5%-15% CAGR).
• Common modeling errors: aggressive revenue growth, missing capex, ignoring working capital, optimistic exit multiple, missing transaction costs.

What an acquisition model actually does

An acquisition model (often called an LBO model) takes a target business’s historical financials and projects forward to calculate equity returns under specific deal-structure and operational assumptions. The model has 5-7 interconnected components that together produce the buyer’s underwriting case: sources & uses, P&L projections, debt schedule, working capital schedule, capex schedule, equity returns waterfall, and sensitivity analysis. Changing any input flows through to the output (returns), making the model interactive.

Why models matter. The model is the underwriting tool: it forces the buyer to make explicit assumptions about every financial dimension of the deal. Revenue growth: 8% CAGR or 12% CAGR? Margin expansion: 50bps annually or 200bps? Capex: 3% of revenue or 5%? Each assumption is a bet. The model aggregates the bets into a single return projection (IRR, MOIC). Without the model, buyers operate on intuition and heuristics; with the model, buyers test their thesis explicitly.

Stakeholders for the model. Internal: deal team uses model for underwriting decisions, partners use for investment committee approval. External: senior lenders use model to evaluate debt capacity and underwriting; LPs use model to evaluate per-deal investment decision; sellers’ bankers occasionally see model summary to understand buyer’s underwriting (rarely full model). Each stakeholder has different views: lenders care about debt service coverage; LPs care about returns waterfall; deal teams care about everything.

Model build vs reuse. First-time buyers typically build models from scratch (4-8 weeks for first model). Established buyers reuse models from prior deals (1-2 weeks per new deal with proper template). Most LMM PE platforms have proprietary model templates with sector-specific add-ons (e.g., commercial HVAC model template, dental practice template). Templates accelerate work but can ossify thinking; periodically rebuilding from scratch keeps the model fresh.

Common model platforms. Microsoft Excel: dominant platform; 90%+ of LMM models use Excel. Strengths: flexibility, transparency, universal accessibility. Weaknesses: error-prone, no version control, calculation chain risks. Specialized tools: eFront (used by some PE platforms), Pitchbook Modeling, MackeyRMS, Capital IQ. Strengths: structured calculations, audit trails, sometimes integration with research databases. Weaknesses: less flexible, rigid templates, enterprise pricing. Most LMM buyers stay in Excel; large institutional firms sometimes use specialized tools.

Model accuracy vs precision. An acquisition model produces 5-year projections of complex business performance. The output (24.7% IRR, 3.42x MOIC) projects false precision. The right way to interpret: the model’s directionally correct — this is a 20-25% IRR, 3-4x MOIC deal, not a 10% IRR deal. Sensitivity ranges matter more than point estimates. Buyers who treat point estimates as gospel make worse decisions than buyers who understand the underlying ranges.

Sources & uses: the deal capital structure

Sources & uses is the first section of every acquisition model. It accounts for all cash flowing into the deal at close (sources) and all cash flowing out (uses). Sources must equal uses. The structure determines leverage, equity check size, and seller financing dynamics — and constrains every other model component.

Sources: senior secured debt. Senior debt is the first capital source: 3-4x leverage on EBITDA from commercial bank or BDC at SOFR + 350-450bps. Examples: senior bank loan (Wells Fargo, Bank of America, regional banks) at $5-25M for sub-$50M EV deals; senior tranche of unitranche structure at $25-300M for larger LMM deals. Pricing 2026: 7-9% all-in rate on senior debt. Senior debt has tightest covenants, cheapest cost, fastest amortization.

Sources: subordinated/mezzanine or unitranche. Junior debt provides additional leverage above senior. Traditional structure: separate mezzanine debt at 12-14% all-in. Modern LMM standard: unitranche structure (combined senior + sub in one tranche) at 4-6x total leverage, SOFR + 500-700bps (9.5-11% all-in). Most LMM deals in 2026 use unitranche from direct lenders (Ares Capital, Blue Owl, Antares, Twin Brook, Apollo, Carlyle, KKR). See our unitranche guide for details.

Sources: seller note. Seller note: subordinated promissory note from buyer to seller, typically 10-25% of EV, 5-7 year amortization, 6-9% interest. Used in sub-$100M EV deals to bridge between buyer’s debt capacity and total purchase price. Examples: $20M EV deal with $10M senior debt + $4M seller note + $6M buyer equity. Seller note: subordinated to all senior debt, sometimes with personal guarantee from buyer, sometimes with security interest in second lien on assets.

Sources: buyer equity. Buyer equity: residual capital structure component. Equals total deal cost minus debt sources minus seller note minus close-out adjustments. Typical structure: 30-40% equity for typical LMM deal. Lower equity (20-25%): high-quality recurring-revenue businesses with strong debt capacity. Higher equity (40-50%): asset-light service businesses with limited debt capacity, or sub-$25M EV where SBA dominates.

Sources: rollover equity (if applicable). Rollover equity: seller’s retained equity in post-acquisition business, typically 10-30% of post-close equity. Rollover reduces buyer’s cash equity check. Example: $100M EV deal with $50M debt + $30M cash equity + $20M rollover equity. Buyer’s cash equity = $30M (vs $50M without rollover). See rollover equity guide for structuring details.

Uses: purchase price. Purchase price: EBITDA × multiple. Example: $20M EBITDA × 5.5x = $110M EV. Purchase price is the largest single use; everything else is incremental. Note: purchase price flows from EBITDA assumption (which may be adjusted EBITDA with add-backs, see below) and multiple assumption (which depends on sector, size, growth, and competitive dynamics).

Uses: transaction costs. Transaction costs: 1-2% of EV typical. Includes legal fees ($150-500K depending on deal complexity), advisory fees (Quality of Earnings $50-150K, sector advisor $25-100K, intermediary fees if any), debt arrangement fees (1-2% of debt issued), other diligence costs (environmental, IT, customer interviews). For $100M EV deal: $1-2M of transaction costs. Often paid by acquisition entity, reducing net cash to seller.

Uses: working capital adjustment. Working capital adjustment: cash needed at close to fund post-close operations. Mechanics: target normal working capital level (negotiated in PSA), buyer pays target level at close. If actual working capital at close is below target, buyer pays incremental cash; if above, seller refunds. Standard target: 30-60 days of normal operating working capital. On $50M revenue business: $4-8M typical.

Uses: refinancing existing debt. Refinancing existing debt: target’s existing debt typically refinanced at close (replaced with new acquisition debt). On a target with $30M existing debt: $30M of cash flows out to refinance, $30M of new debt comes in (possibly different structure). Net effect on balance sheet: existing debt replaced with new debt; doesn’t affect net cash equity required.

Uses: cash to seller. Cash to seller: residual after all other uses. Calculated as: purchase price – assumed liabilities + working capital adjustment – existing debt refinanced – transaction costs charged to seller (some are charged to acquisition entity). On $100M EV deal with $80M total uses to seller: cash to seller = $80M; balance is rollover equity ($20M).

P&L projections: the operational thesis in numbers

P&L projections translate the operational thesis into 5-7 year projected financials. Revenue assumptions, margin assumptions, capex, working capital — each driven by the buyer’s view on what the business will achieve through the hold. The model produces year-by-year EBITDA, which drives cash available for debt service and exit valuation.

Historical financials. Start with 24-36 months of historical financials: monthly P&L (or annual if monthly unavailable), balance sheet, cash flow. Identify trends: revenue growth, margin expansion/compression, customer mix changes, seasonality. Adjust for one-time items: extraordinary expenses, owner-related expenses, M&A transaction costs. The cleaned historical baseline is the foundation for projections.

Adjusted EBITDA calculation. Apply standard add-backs to convert reported EBITDA to adjusted EBITDA: owner compensation normalization (replace owner’s above-market salary with market-rate salary for replacement; subtract for any owner perks below market), one-time legal/professional fees (M&A costs, restructuring), pre-acquisition add-on EBITDA (if relevant), management fees from sponsor (excluded from EBITDA), normalized rent (if owner-owned property at below-market rent). Document each add-back with rationale; lender QoE will challenge add-backs.

Revenue projections: components. Revenue = volume × price. Volume: customer count, units sold, transaction count. Price: average revenue per unit, price increases. Project each component separately: customer growth (organic + churn), price increases (CPI + value-based), mix changes (premium products, premium service tiers). Aggregate to total revenue. Most LMM businesses grow 5-15% organically; model should project realistic growth based on sector benchmarks and target’s specific positioning.

Revenue projections: build vs benchmark. Bottoms-up build: project revenue from operational drivers (customers, units, prices). Top-down benchmark: project revenue from sector growth + market share. Most LMM models use bottoms-up build; sector growth provides sanity check. Aggressive vs realistic: aggressive growth (15%+ CAGR) requires specific operational thesis; realistic growth (5-10% CAGR) is the LMM default for most sectors.

Add-on acquisitions in revenue. If platform thesis includes add-on acquisitions, project add-on contribution: typical add-on size, deal pace (e.g., 1 add-on per year), integration timeline, full-year revenue contribution. Add-on revenue is incremental to base business growth. Many platform models show: organic 8% growth + 2 add-ons annually = 25% total revenue growth. Add-ons require explicit capital structure (incremental debt, additional equity), accounted in sources & uses for the add-ons.

Margin projections. EBITDA margin = EBITDA / revenue. Project trajectory: starting margin (Year 0 = current), evolution through hold. Operational improvements: cost reduction programs, pricing improvement, scale economies, technology efficiency. Typical LMM margin improvements: 50-200bps over 5-7 year hold. Aggressive: 300-500bps (requires explicit operational thesis). Realistic: 100-200bps from gradual improvement.

Capex assumptions. Capex (capital expenditure): cash spent on equipment, vehicles, property improvements, IT systems. Two categories: maintenance capex (replacing aging equipment, normal operations), growth capex (expanding capacity, new locations, new technology). Maintenance: typically 1-3% of revenue. Growth: variable depending on thesis, 0-5% of revenue. Total capex: 2-5% of revenue typical for LMM. Capex reduces cash available for debt service and equity returns.

Working capital projections. Working capital = accounts receivable + inventory – accounts payable. Working capital builds as revenue grows: 30-60 days of receivables minus 20-45 days of payables. Incremental working capital required = (incremental revenue) × (working capital days / 365). On $5M revenue growth with 45 days of working capital: incremental WC required = $5M × 45/365 = ~$616K. Working capital reduces cash available for debt service.

Free cash flow calculation. Free cash flow = EBITDA – cash interest – cash taxes – capex – incremental working capital – mandatory amortization. The cash flow available to either: pay down debt (excess cash flow sweep), distribute to equity holders, or build cash reserves. Free cash flow drives debt paydown and equity return acceleration. Most LMM models project FCF year-by-year through the hold.

Debt schedule: amortization, interest, paydown

The debt schedule tracks each debt tranche through the hold: starting balance, scheduled amortization, voluntary prepayment, ending balance, interest expense. Most LMM models track 2-4 separate debt tranches (senior, unitranche, sub, seller note) through 5-7 years. Excel implementation: each tranche has its own row block; aggregated to total debt schedule.

Senior debt schedule. Standard senior debt: 5-7 year term, 1% annual amortization (term loan B style), bullet at maturity. Year 1: starting balance (e.g., $30M), 1% amortization ($300K), interest at SOFR + 350bps (assume 7.5% all-in = $2.25M), ending balance ($29.7M). Track through hold; usually refinanced or repaid at exit.

Unitranche debt schedule. Unitranche: similar structure to senior debt schedule but at higher rate. 5-7 year term, 1% annual amortization, bullet at maturity. Year 1: starting balance (e.g., $50M), 1% amortization ($500K), interest at SOFR + 600bps (10% all-in = $5M), ending balance ($49.5M). Higher interest expense than senior debt; same amortization profile.

Mezzanine debt schedule (if separate). Mezz/sub debt: 7-year term, no amortization (bullet at maturity), 12-14% all-in (cash interest 11-12% + PIK 1-3%). Year 1: starting balance (e.g., $15M), 0 amortization, cash interest $1.65M (11%), PIK accrual $300K (2%), ending balance $15.3M. PIK accrual increases balance over time, increasing future interest expense. Most LMM deals avoid mezz in 2026; unitranche replaces it.

Seller note schedule. Seller note: 5-7 year term, 5-10% amortization annually, 6-9% interest. Year 1: starting balance ($15M), 10% amortization ($1.5M), interest 8% on declining balance ($1.2M), ending balance $13.5M. Subordinated to senior; usually paid current rather than PIK. Default risk: seller has personal guarantee from buyer in many cases.

Excess cash flow sweep. Mandatory paydown from excess cash flow: most senior credit agreements require 50-75% of excess cash flow to repay senior debt if leverage above target. Excess cash flow = FCF – mandatory amortization – capex – permitted distributions. If platform generates $5M excess cash flow, and sweep is 50%, then $2.5M paid down to senior debt (above scheduled amortization). Accelerates debt repayment; reduces equity returns timing slightly but improves leverage profile.

Voluntary prepayment. Buyer may voluntarily prepay debt above scheduled amortization. Standard prepayment penalties: 102/101/par over years 1-3, par thereafter for unitranche; varies for senior. Voluntary prepayment reduces interest expense and accelerates debt paydown but triggers prepayment penalty if in early years. Most buyers avoid voluntary prepayment in years 1-2 unless economically advantageous.

Refinancing during the hold. Mid-hold refinancing: many platforms refinance debt year 3-4 to take advantage of improved performance and rates. Mechanics: existing debt repaid (with prepayment penalty if applicable), new debt issued at improved rates and terms. Year 3 refinancing: existing $40M unitranche at 10% replaced with new $50M unitranche at 8.5%. Cash savings: $50M × 1.5% = $750K annually. Plus accordion capacity for add-ons.

Total debt and interest expense. Aggregate all tranches to total debt balance and total interest expense per year. Year 1: $50M unitranche + $15M seller note = $65M total debt; $5M + $1.2M = $6.2M total interest. Year 5: depending on amortization and refinancing, total debt $35-50M; total interest $3.5-5M. Track total debt service coverage ratio (FCF / debt service) to ensure compliance with covenants.

Equity returns waterfall: IRR and MOIC across scenarios

The equity returns waterfall calculates buyer’s IRR and MOIC across deal scenarios. IRR (internal rate of return): annualized return over the hold period. MOIC (multiple of invested capital): total cash returned divided by equity invested. Both metrics matter: IRR captures time, MOIC captures absolute returns. PE buyers report both.

Exit valuation calculation. Exit value = exit EBITDA × exit multiple. Exit EBITDA: projected EBITDA in exit year (typically year 5-7). Exit multiple: assumed multiple at exit. Standard assumptions: exit multiple equal to entry multiple (multiple expansion = 0), or exit multiple 0.5-1x above entry (modest multiple expansion). Aggressive: 1-2x multiple expansion (assumes platform creates value through scale and quality). Conservative: 0.5-1x multiple compression.

Exit equity value. Exit equity value = exit EV – exit debt – exit transaction costs. Exit EV: from EBITDA × multiple. Exit debt: remaining debt balance at exit (after years of amortization and excess cash flow sweep). Exit transaction costs: legal, advisory, broker fees, typically 1-2% of EV. Equity value: remaining cash to equity holders after all debt and costs.

Equity allocation. Equity proceeds split between buyer equity (PE/family office/searcher) and rollover equity (seller). Pro-rata based on ownership %: e.g., buyer holds 80% of equity, seller holds 20% rollover. Buyer’s exit value = total exit equity × 80%. Plus distributions during hold (rare for typical 5-7 year LMM deal but happens for longer holds).

IRR calculation. IRR = annualized return given cash flows at specific dates. Initial cash out: equity check at close (e.g., $30M day 0). Cash returns: distributions during hold (often $0 for first 5 years), exit value (e.g., $90M at year 5). IRR formula in Excel: =IRR(cash flow series). On $30M out / $90M back over 5 years: IRR ~25%. IRR is sensitive to timing — faster exits boost IRR even with same MOIC.

MOIC calculation. MOIC = total cash returned / equity invested. On $30M invested / $90M total returned: MOIC = 3.0x. Cash returned includes: exit proceeds + cumulative distributions during hold. MOIC ignores time value: 3.0x in 3 years is much better than 3.0x in 7 years (despite same MOIC). Always report both IRR and MOIC together.

Standard scenario set. Most models project 3 scenarios: base case (most likely), upside case (favorable), downside case (unfavorable). Base case: 8-10% revenue growth, 100bps margin improvement, exit at entry multiple. Upside: 12-15% growth, 200bps margin improvement, exit 1x multiple expansion. Downside: 3-5% growth, flat margins, exit 0.5x multiple compression. Each scenario produces IRR and MOIC.

Target returns by buyer archetype. PE platforms (LMM): 20%+ IRR, 3x+ MOIC base case. Family offices: 15-18% IRR, 2.5-3x MOIC base case (longer hold flexibility, less performance pressure). Search funders: 25%+ IRR, 4x+ MOIC base case. Independent sponsors: similar to PE platforms. Strategic buyers: variable based on synergies (often lower headline IRR with strategic value justifying).

Investment committee benchmarks. Most PE platforms reject deals where base case fails to deliver minimum threshold returns (typically 18-20% IRR). Marginal deals (15-18% IRR base case) require operational thesis with high conviction. Strong deals (22%+ IRR base case) advance to full diligence. Exceptional deals (25%+ IRR base case) become priorities. Returns benchmarks adjust by sector and risk profile.

Sensitivity analysis: what assumptions matter most

Sensitivity analysis tests how returns change when key assumptions vary. Standard sensitivities: exit multiple (5x to 7x EBITDA), EBITDA growth (5% to 15% CAGR), entry multiple (4x to 6x EBITDA), capex assumptions (3% to 6% of revenue), debt structure changes. Sensitivity tables reveal which assumptions drive returns most heavily — informing where to focus diligence.

Standard 5×5 sensitivity matrix. Most common sensitivity: exit multiple (rows) × EBITDA growth (columns). Row values: 4.5x, 5.0x, 5.5x, 6.0x, 6.5x exit multiple. Column values: 5%, 8%, 10%, 12%, 15% revenue growth. Each cell: IRR or MOIC at that combination. Reveals: which combinations produce target returns; sensitivity to multiple expansion vs growth.

Multiple expansion sensitivity. Test: how does return change with exit multiple? Base case: 5.5x exit multiple = X% IRR. Multiple compression to 5.0x: IRR drops by 200-400bps. Multiple expansion to 6.0x: IRR rises by 200-400bps. Sensitivity reveals: how dependent are returns on multiple expansion? Aggressive deals depend heavily; conservative deals are robust to multiple compression.

EBITDA growth sensitivity. Test: how does return change with EBITDA growth? Base case: 10% revenue growth + 50bps margin expansion = 12% EBITDA growth. Growth slowdown to 6% revenue: EBITDA grows 7%, returns drop materially. Growth acceleration to 15% revenue: EBITDA grows 17%, returns rise materially. Reveals: how dependent are returns on growth thesis? Deals dependent on >15% growth require validation.

Entry multiple sensitivity. Test: how does return change with entry multiple? Higher entry multiple (6.5x vs 5.5x): pays $1M more EBITDA at entry, returns drop 100-200bps IRR. Lower entry multiple (4.5x vs 5.5x): pays $1M less, returns rise 100-200bps IRR. Sensitivity reveals: how much pricing flexibility does the model have? Entry-multiple sensitivity informs negotiating posture.

Capex sensitivity. Test: how does return change with capex assumptions? Higher capex (5% vs 3% of revenue): less FCF, slower debt paydown, lower returns. Lower capex (2% vs 3%): more FCF, faster paydown, higher returns. Sensitivity reveals: how dependent on capex assumptions? Asset-heavy businesses require accurate capex projections; asset-light businesses less sensitive.

Debt structure sensitivity. Test: how does return change with debt structure? Higher leverage (5.5x vs 4.5x): more equity returns from financial engineering, also more risk. Lower leverage: lower returns, lower risk. Cheaper debt (75bps tighter pricing): higher returns, all else equal. Sensitivity reveals: trade-off between leverage and risk; tightness of debt cost matters.

Hold period sensitivity. Test: how does return change with hold period? Faster exit (3 years vs 5 years): higher IRR even with same MOIC. Slower exit (7 years vs 5 years): lower IRR even with same MOIC. Sensitivity reveals: how dependent on exit timing? Most LMM deals: 5-year hold is base case; 3-year early exit and 7-year extended hold are alternative scenarios.

Tornado chart: ranking sensitivities. Tornado chart: ranks sensitivities by impact on IRR. Typical rankings: exit multiple (highest impact on IRR), EBITDA growth (high impact), entry multiple (high impact), debt structure (medium impact), capex (low-medium impact), working capital (low impact). Tornado chart focuses diligence on highest-impact assumptions.

Common modeling errors that destroy returns

Below are the most common acquisition modeling errors that destroy buyer returns or credibility. Each is preventable with disciplined execution. Many come from time pressure, junior team execution without senior review, or confirmation bias toward target deals.

Error 1: aggressive revenue growth. Symptom: model projects 15-20% annual revenue growth without operational thesis to justify. Cause: deal momentum, unfamiliar sector, optimistic salesperson narrative. Impact: model produces favorable returns based on unrealistic growth; deal closes; reality misses; returns disappointing. Prevention: anchor revenue growth to sector benchmarks (IBISWorld, sector reports), validate against historical track record, require explicit operational thesis for above-sector growth.

Error 2: missing or under-modeled capex. Symptom: model assumes 1-2% of revenue for capex when actual maintenance capex is 4-5%. Cause: didn’t dig into asset base, accepted seller’s representations, focused on revenue growth ignoring asset replacement. Impact: less FCF than modeled, slower debt paydown, lower returns. Prevention: review historical capex (last 3-5 years), benchmark against sector capex intensity, identify aging assets requiring replacement, separate maintenance from growth capex.

Error 3: ignoring working capital build. Symptom: model assumes static working capital despite revenue growth. Cause: simplification for first-pass model, didn’t realize working capital scales with revenue. Impact: cash flow overstated, debt service modeling incorrect, returns inflated. Prevention: explicit working capital schedule (days-of-revenue calculation), incremental working capital line in cash flow build.

Error 4: optimistic exit multiple. Symptom: model assumes 1-2x multiple expansion at exit (entry 5.5x, exit 6.5-7.5x). Cause: belief in operational improvements that justify multiple expansion. Impact: returns dependent on multiple expansion that may not happen; lender skepticism. Prevention: model base case at entry multiple (no expansion); upside case at +0.5x; downside at -0.5x. Multiple expansion should be tested, not assumed.

Error 5: missing transaction costs. Symptom: model accounts for only purchase price; ignores legal, advisory, debt arrangement fees. Cause: simplification, time pressure. Impact: equity check size understated by 1-2%; returns slightly inflated. Prevention: explicit transaction cost line item (1-2% of EV), include all known costs.

Error 6: missing tax effects. Symptom: model uses pre-tax EBITDA as cash flow proxy; ignores cash taxes. Cause: simplification. Impact: overstates cash available for debt service and equity returns. Prevention: explicit cash tax line item (typically 21% corporate rate on net income, with adjustments for tax shield from interest, depreciation, M&A costs).

Error 7: aggressive add-back acceptance. Symptom: model accepts seller’s claimed adjusted EBITDA without scrubbing. Cause: time pressure, didn’t have access for full diligence. Impact: leverage and pricing based on inflated EBITDA; QoE pulls back; deal re-trades or dies. Prevention: structured add-back review with QoE; scrutinize each add-back; common rejected add-backs include ‘projected synergies,’ ‘one-time items’ that recur, ‘stock-based comp’ included as non-cash.

Error 8: hardcoded assumptions in formulas. Symptom: revenue growth rate hardcoded in formula rather than driven by input cell. Cause: lazy modeling, time pressure. Impact: assumption changes require formula edits; sensitivity analysis impossible; model errors compound. Prevention: every assumption in dedicated input cell; formulas reference inputs; clear distinction between inputs and formulas.

Error 9: circular references. Symptom: interest expense depends on debt balance, debt balance depends on cash flow, cash flow depends on interest expense. Cause: improper modeling sequence. Impact: Excel iteration risk, errors hard to debug. Prevention: explicit circular reference solving (Excel iteration enabled), or break circularity through interest-on-average-balance assumption (slight inaccuracy, simpler structure).

Error 10: not stress-testing covenant compliance. Symptom: model shows base case covenant compliance; doesn’t test downside cases. Cause: focus on returns, less on covenant risk. Impact: deal closes, downside hits, covenant trips, expensive amendments. Prevention: explicit covenant compliance check across all scenarios (base, upside, downside, stress); ensure 20%+ headroom on key covenants in base case.

Model construction: 4-week build for first-time buyers

First-time buyers typically need 4-6 weeks to build a functional acquisition model. Established buyers reuse templates and complete models in 1-2 weeks per deal. Below is the 4-week build framework for first-time models.

Week 1: financial historical data and adjusted EBITDA. Gather 24-36 months of historical P&L, balance sheet, cash flow. Construct historical financials in standard format. Identify and document adjusted EBITDA add-backs with rationale. Validate add-backs against tax returns and bank statements (rough sanity check). Build historical trend analysis (growth, margins, working capital, capex).

Week 2: sources & uses, capital structure, debt schedule. Construct sources & uses table. Project debt structure (senior, unitranche/sub, seller note, equity). Build debt schedule for each tranche through hold (5-7 years). Calculate interest expense and amortization. Confirm sources = uses (balance check).

Week 3: P&L projections, working capital, capex. Project revenue 5-7 years (organic growth, price/volume, add-ons). Project EBITDA margins (operational thesis). Project capex (maintenance + growth). Project working capital build (days-of-revenue). Build year-by-year P&L. Calculate free cash flow.

Week 4: returns waterfall, sensitivities, scenarios. Calculate exit value (EBITDA × multiple). Calculate exit equity value (exit EV – debt – costs). Calculate IRR and MOIC. Build base / upside / downside scenarios. Construct 5×5 sensitivity matrices (exit multiple × EBITDA growth). Construct tornado chart of sensitivities. Document key assumptions and supporting rationale.

Quality checks before sharing. Sources = uses (balance check). Adjusted EBITDA tied to historical sources. Revenue projections reasonable vs sector benchmarks. Margin projections reasonable vs operational thesis. Capex/working capital tied to revenue. Debt schedule self-consistent (starting balance, amortization, ending balance). IRR and MOIC reasonable for sector. Sensitivity ranges appropriate. Documentation of key assumptions complete.

Sharing the model externally. Lender models: simplified version focused on debt service coverage and covenant compliance; remove buyer-specific equity returns. LP models (independent sponsor, search fund): focus on equity returns waterfall, scenarios, sponsor track record context. Investment committee: full model with all assumptions, sensitivities, scenarios.

Iterating models with new diligence. Models update as diligence proceeds: QoE finds adjustments to historical EBITDA; customer interviews validate revenue assumptions; operational diligence refines capex and working capital. Each diligence finding feeds back into model. Final model at LOI signing reflects diligence-confirmed assumptions; model may shift again at PSA negotiation. Track model versions to maintain history.

Model variations by buyer type

Acquisition models vary by buyer archetype and strategy. Below are the variations from the standard LBO model framework, tailored to different buyer types.

Search fund model. Variations from standard LBO: equity check from search investors (10-20 LPs), pref structure for search investors (typically 8% pref + 70/30 split with searcher), search fund-specific compensation (searcher’s salary during ownership, separate from equity returns). Hold period: 5-15 years (longer than typical LMM PE). Exit assumption: more conservative (multiple compression possible if sector matures). Returns target: 25%+ IRR / 4x+ MOIC.

Family office direct model. Variations: longer hold period (8-15 years vs 5-7 for PE), no fund-level fees (family office bears costs directly), equity returns flow direct to family office (no LP waterfall complexity). Returns target: 15-18% IRR / 2.5-3x MOIC. Capital structure: often more equity-heavy (30-40% equity) because family office isn’t trying to maximize IRR through leverage.

PE platform with add-ons model. Variations: explicit add-on acquisition assumptions (1-3 add-ons over 5-7 years), incremental capital structure for each add-on (additional debt, additional buyer equity), platform-level synergies and operational improvements. Returns aggregate across base business + add-ons. Sensitivities include: add-on count, add-on sizing, integration timing, post-integration synergies.

Independent sponsor model. Variations: equity raised deal-by-deal from LPs (separate LP base for each deal), transaction fee paid at close, standard pref + promote structure (8% pref, 20-25% sponsor promote). Equity returns waterfall split between LPs and sponsor. Sensitivities include: capital raise risk (probability of full capital raise success), LP-specific terms.

Strategic buyer model. Variations: synergy assumptions (cost reduction from integration, revenue synergies from cross-sell), integration costs (one-time transaction-related), parent company financing (no separate debt structure if integrated). Returns measured as: incremental EBITDA from acquisition + synergies, vs purchase price + integration costs. Hold period: indefinite (typically not modeled as exit).

Roll-up / consolidation model. Variations: model multiple acquisitions in sequence (platform + 5-15 add-ons over 5-7 years), platform-level capital structure with accordion features for add-ons. Returns measured at platform level (total equity invested across platform + add-ons; total exit value). Sensitivities include: add-on pace, add-on multiples, post-integration synergies, platform-level operational improvements.

Distressed / turnaround model. Variations: starting EBITDA is depressed (current state); recovery assumptions over 18-36 months. Restructuring costs (severance, asset disposition, legal). Possible bankruptcy structures (363 sales). Risk-adjusted returns: higher target IRR (25-30%+) given execution risk. Sensitivities focus on: timing of recovery, depth of trough EBITDA, terminal value at exit.

ESOP transaction model. Variations: ESOP buys stock from seller, financed by senior debt and ESOP-specific debt structures (Section 1042 election). No promote / carry (ESOP is the acquirer; no separate equity). Returns measured for the ESOP entity, not for individual investors. Specific tax considerations (Section 1042 deferral, ESOP tax exemption).

Investment committee presentation: model in context

An acquisition model lives within an investment committee presentation that contextualizes the numbers. Below is the standard PE / family office / search fund investment committee presentation structure that integrates with the model.

Section 1: deal summary (1-2 pages). Target description, deal source (broker, advisor, partner, direct), proposed structure (EV, leverage, equity check), proposed returns (IRR, MOIC base case), key thesis. High-level overview for committee members to orient. Should answer: what are we buying, why, and what return do we expect?

Section 2: business overview (3-5 pages). Industry background, target’s positioning, competitive landscape, customer base, employee structure, geographic footprint. Provides context for committee members to evaluate operational thesis. Should connect target’s characteristics to investment thesis.

Section 3: financial profile (3-5 pages). Historical financials (24-36 months), adjusted EBITDA build, revenue trends, margin trends, capex history, working capital pattern. Plus diligence findings (QoE adjustments, operational considerations). Establishes financial baseline that drives projections.

Section 4: investment thesis and operational plan (2-4 pages). Specific operational improvements expected: revenue growth drivers, margin expansion plan, capex strategy, working capital optimization, technology/process improvements. Connects model’s projection assumptions to operational reality. Without compelling thesis, model projections are arbitrary.

Section 5: model output and sensitivities (4-6 pages). Sources & uses table. P&L projections summary (5-7 year revenue, EBITDA, capex, FCF). Debt schedule. Returns waterfall (IRR, MOIC by scenario). Sensitivity matrices (5×5 grids). Tornado chart. Quantifies the deal economics across scenarios.

Section 6: risks and mitigants (1-2 pages). Top 5-7 risks: customer concentration, key person dependency, regulatory changes, competitive dynamics, sector cyclicality, execution risk on operational thesis, integration risk for add-ons. Each risk: probability assessment, potential impact, mitigation strategies. Demonstrates buyer awareness of downside scenarios.

Section 7: structure and governance (1-2 pages). Deal structure (asset vs stock, rollover terms, employment agreements, non-competes). Post-close governance (board composition, reporting, key decisions requiring approval). Capital structure detail (debt provider, terms, covenants). Confirms buyer’s negotiating position and post-close operating plan.

Section 8: timeline and next steps (1 page). Diligence completion timeline. PSA negotiation timeline. Closing timeline. Capital deployment expectations. Identifies remaining work and resource needs. Shows committee that path from approval to close is feasible.

Modeling tools and resources

Below are common modeling tools, templates, and resources for buyers building acquisition models. Choice of tool depends on team size, technical sophistication, deal frequency, and existing infrastructure.

Microsoft Excel. Dominant LMM modeling platform. Strengths: ubiquitous, flexible, widely understood. Weaknesses: error-prone, no version control, no audit trail. Best for: 90%+ of LMM PE platforms, family offices, search funders, independent sponsors. Typical model size: 8-15 sheets, 5,000-15,000 formulas. Template availability: many publicly-available LBO templates from Wall Street Prep, Macabacus, Breaking Into Wall Street, sector-specific from advisor firms.

eFront. Specialized PE modeling and portfolio management platform owned by BlackRock. Strengths: structured calculations, audit trails, integration with portfolio operations. Weaknesses: rigid templates, expensive ($1,000-5,000+ per user per year), enterprise-focused. Best for: large institutional PE platforms with multi-fund infrastructure.

Pitchbook Modeling. Pitchbook’s modeling tool for deal analysis. Strengths: integrated with Pitchbook’s database for sector benchmarks and comparables, clean templates. Weaknesses: less flexible than Excel, requires Pitchbook subscription. Best for: PE platforms heavily using Pitchbook for research.

MackeyRMS. Investment research and modeling platform. Strengths: structured workflow, audit trails, collaboration features. Weaknesses: less flexible, expensive. Best for: institutional asset managers and PE platforms with dedicated research teams.

Capital IQ. S&P Global’s investment research and modeling platform. Strengths: extensive comparable data, integrated screening, structured templates. Weaknesses: expensive ($30,000+ annually), Excel-style flexibility limited within platform. Best for: institutional users with broad research needs.

Modeling courses and training. Wall Street Prep: comprehensive online courses on LBO modeling, M&A modeling, advanced Excel. $300-1,500 per course. Breaking Into Wall Street: similar offering with focus on banking and PE. Macabacus: free resources plus paid Excel add-ins. CFA Institute: financial modeling content within broader curriculum. New analysts typically complete 2-4 weeks of formal training before working on live models.

Sector-specific resources. IBISWorld, MarketResearch.com, Statista: sector-level data for revenue projections. Hoovers, D&B: company-level financial estimates for benchmarks. PitchBook, Mergermarket: comparable transaction multiples for valuation. Sector-specific advisors and research firms: deeper sector intelligence for operational thesis.

Internal team resources. Established PE platforms have proprietary template libraries: standard LBO templates by sector, sensitivity analysis macros, comparable transaction databases, sector benchmark databases. Junior analysts adapt templates to specific deals; senior partners review for accuracy. Template maintenance is ongoing investment for any platform doing 5+ deals annually.

Stress testing and downside scenarios: what could go wrong

Beyond standard scenarios (base, upside, downside), sophisticated buyers stress-test models against specific adverse events. Stress testing reveals deal vulnerability and informs negotiating posture, covenant structure, and reserve requirements. Below is the framework for building stress scenarios into the model.

Customer concentration stress. Test: top customer (typically 15-30% of revenue in LMM) leaves at end of year 2. Impact: revenue drops 15-30% in year 3, EBITDA falls disproportionately (fixed cost base), debt service stresses. Model output: covenant compliance? Liquidity? Required equity infusion? Most LMM deals should survive top-customer loss without covenant breach; deals that don’t are too risky.

Recession / demand shock. Test: revenue declines 15-25% in years 2-3 (mimicking recession), recovers years 4-6. Impact varies by sector: cyclical sectors (industrial, construction) hit hard; recurring revenue sectors (healthcare services, software) more resilient. Model: covenant headroom, debt service coverage, equity returns at exit. Deals that turn unprofitable in recession require larger equity cushions.

Interest rate stress. Test: SOFR rises 200bps over years 2-3 (interest expense rises proportionally). Impact: free cash flow drops, debt service tightens, refinance options compromised. Most 2026 LMM deals priced at SOFR + 600bps survive +200bps SOFR move with some compression in returns. Deals at maximum leverage at origination break under rate stress.

Multiple compression at exit. Test: exit multiple drops 1x EBITDA from base case. Impact: exit equity value drops $20-50M depending on size. Returns deteriorate: 200-400bps IRR loss. Model: at what exit multiple does the deal still produce target returns? At what exit multiple does the deal lose money for buyer? This ‘breakeven multiple’ analysis informs entry-multiple discipline.

Capex shock. Test: required capex rises 50% from base case (e.g., regulatory requirement, technology refresh). Impact: less FCF, slower debt paydown, lower returns. Model output: covenant compliance under capex stress; ability to absorb capex without external financing.

Working capital stress. Test: working capital days extend (e.g., AR days from 45 to 60 due to customer payment slowdown). Impact: cash absorbed by working capital build, less FCF. Model: liquidity adequacy under working capital extension; need for revolver capacity.

Combined stress (recession + customer loss + rate rise). Test: simultaneous adverse events (severe but realistic combination). Most LMM deals can survive single stresses; fewer survive combined stresses. Model: at what combination of adverse events does the deal fail (covenant breach, liquidity crisis, equity loss)? Establishes the ‘failure boundary’ that informs deal pricing and structure.

Stress test outputs and decision. Stress test outputs inform: deal pricing (less paid for more downside risk), capital structure (more equity in stressed deals), covenant negotiation (more headroom in stressed deals), reserve requirements (more cash held back at close). Deals that fail multiple stress tests should be repriced, restructured, or rejected. Stress testing is uncomfortable but essential; investment committees that skip it close worse deals.

Conclusion

Acquisition modeling is the buyer’s underwriting backbone — the tool that translates a target’s financials into projected returns under specific deal-structure and operational assumptions. The standard LMM acquisition model integrates 5-7 components: sources & uses (debt + equity + seller note), P&L projections (revenue + margins + capex + working capital), debt schedule (amortization + interest + paydown), equity returns waterfall (IRR + MOIC across scenarios), and sensitivity analysis (multiple expansion + EBITDA growth). Standard 2026 capital structure: 50% senior/unitranche debt + 30% buyer equity + 20% seller note + close-out cash. Returns targets vary by archetype: PE platforms 20%+ IRR / 3x+ MOIC, family offices 15-18% / 2.5-3x, search funders 25%+ / 4x+. Common modeling errors — aggressive revenue growth, missing capex, ignoring working capital, optimistic exit multiple, missing transaction costs, missing tax effects, aggressive add-back acceptance — destroy returns and credibility. Disciplined buyers test models against multiple scenarios (base/upside/downside) and stress-test covenant compliance. The model is a tool, not an answer: it reveals what assumptions need to be true for the deal to deliver target returns and tests what happens when assumptions break. Buyers who use models this way make better decisions; buyers who treat point estimates as gospel chase deals into bad outcomes. The framework above — 4-week build for first-time buyers, weekly cadence for established buyers with templates, integration with investment committee presentations — comes from observing which approaches work and which don’t across hundreds of LMM acquisitions. And if you want to source acquisition opportunities that fit your specific modeling assumptions and returns targets, we’re a buy-side partner that delivers proprietary, off-market deal flow to our 76+ buyer network — the sellers don’t pay us, no contract required.

Frequently Asked Questions

What is an acquisition model (LBO model)?

An acquisition model translates a target business’s historical financials into projected returns under specific deal-structure and operational assumptions. It contains 5-7 components: sources & uses (capital structure), P&L projections (revenue, margin, capex, working capital), debt schedule (amortization, interest, paydown), equity returns waterfall (IRR, MOIC), and sensitivity analysis (multiple expansion, EBITDA growth). Most LMM models are built in Excel.

What’s the standard 2026 LMM capital structure?

Typical 2026 LMM deal: 50% senior/unitranche debt + 30% buyer equity + 20% seller note + close-out cash. Senior debt: 3-4x leverage at SOFR + 350-450bps. Unitranche: 4-6x leverage at SOFR + 500-700bps. Seller note: 10-25% of EV, 5-7 year amortization, 6-9% interest. Buyer equity: residual, 20-40% of EV. Variations exist by deal size and sector.

What returns should I target in my LBO model?

PE platforms (LMM): 20%+ IRR, 3x+ MOIC base case. Family offices: 15-18% IRR, 2.5-3x MOIC base case (longer hold flexibility). Search funders: 25%+ IRR, 4x+ MOIC base case. Independent sponsors: similar to PE platforms (20%+ IRR, 3x+ MOIC). Strategic buyers: variable based on synergies. Most investment committees reject deals below 18-20% IRR base case for PE.

How do I calculate IRR vs MOIC?

MOIC (multiple of invested capital) = total cash returned / equity invested. Excludes time. On $30M invested / $90M returned: MOIC = 3.0x. IRR (internal rate of return) = annualized return given timing of cash flows. On $30M invested / $90M returned over 5 years: IRR ~25%. Both metrics matter: IRR captures time value, MOIC captures absolute returns. Always report both together.

What sensitivity analysis should I include?

Standard 5×5 sensitivity matrix: exit multiple (rows: 4.5x-6.5x EBITDA) × EBITDA growth rate (columns: 5%-15% CAGR). Each cell shows IRR or MOIC at that combination. Plus separate sensitivities on: entry multiple, capex assumptions, debt structure, hold period. Plus tornado chart ranking sensitivities by IRR impact (typically: exit multiple highest, EBITDA growth high, entry multiple high, debt structure medium).

What scenarios should I project?

Standard scenario set: base case (most likely outcome based on operational thesis), upside case (favorable assumptions), downside case (unfavorable but realistic). Base: 8-10% revenue growth, 100bps margin improvement, exit at entry multiple. Upside: 12-15% growth, 200bps margin expansion, +0.5x multiple expansion. Downside: 3-5% growth, flat margins, -0.5x multiple compression. Each scenario produces full IRR/MOIC.

What are the most common modeling errors?

Top errors: (1) aggressive revenue growth without operational thesis, (2) missing or under-modeled capex, (3) ignoring working capital build, (4) optimistic exit multiple expansion, (5) missing transaction costs, (6) missing cash tax effects, (7) accepting aggressive seller add-backs without scrubbing, (8) hardcoded assumptions in formulas, (9) circular reference issues, (10) not stress-testing covenant compliance across scenarios.

What’s the right exit multiple assumption?

Conservative default: exit at entry multiple (no expansion). Modest upside: +0.5x expansion (5.5x entry, 6.0x exit). Aggressive: +1x expansion. Multiple expansion should be tested in sensitivity analysis, not assumed. Returns dependent on multiple expansion are riskier; returns based on EBITDA growth are more controllable. Most LMM deals model base case at entry multiple; upside cases include modest expansion.

How long does it take to build an acquisition model?

First-time buyers: 4-6 weeks for first model. Established buyers with templates: 1-2 weeks per new deal. Quick first-pass model: 4-6 hours by experienced analyst. Detailed final model: 80-120 hours of analyst time including diligence iteration. Models are continuously updated through diligence as findings refine assumptions; final model at LOI signing reflects diligence-confirmed inputs.

What modeling tools should I use?

Microsoft Excel: dominant LMM platform; 90%+ of LMM models use Excel. Strengths: flexibility, transparency. Weaknesses: error-prone. Specialized tools: eFront (large institutional PE), Pitchbook Modeling (research-integrated), MackeyRMS, Capital IQ. Most LMM buyers stay in Excel. Templates available from Wall Street Prep, Macabacus, Breaking Into Wall Street, sector-specific advisors.

How do add-on acquisitions affect the model?

Platform thesis with add-ons requires: explicit add-on assumptions (1-3 add-ons over 5-7 years), incremental capital structure for each add-on (additional debt via accordion, additional buyer equity), platform-level synergies, integration timing. Returns measured at platform level (total equity across platform + add-ons; total exit value). Sensitivities include add-on count, add-on sizing, integration timing, post-integration synergies.

How do I model rollover equity in the returns waterfall?

Rollover equity at close: reduces buyer’s cash equity check (e.g., $50M deal with $30M cash + $20M rollover = $30M cash equity required). Through hold: rollover holder shares pro-rata in distributions and exit. At exit: equity proceeds split based on ownership %. If buyer holds 80%, seller’s rollover holds 20%, then exit equity value × 80% = buyer’s exit value. Buyer’s IRR calculated on the cash equity check (excluding rollover), not total equity stake.

How is CT Acquisitions different from a deal sourcer or a sell-side broker?

We’re a buy-side partner, not a deal sourcer flipping leads or a sell-side broker representing the seller. Deal sourcers typically charge buyers a finder’s fee on top of the deal and don’t curate quality. Sell-side brokers represent the seller, charge the seller 8-12% of the deal, and run auction processes that maximize seller proceeds at the buyer’s expense. We work directly with 76+ active buyers — search funders, family offices, lower middle-market PE platforms running LBO models on every deal, and strategic consolidators — and source proprietary off-market deal flow for them at no cost to the seller. The sellers don’t pay us, no contract is required, and we curate deals to fit each buyer’s specific buy box, capital structure, and modeling requirements. You see vetted opportunities that aren’t on BizBuySell or Axial, with a buy-side advocate who knows both sides of the table.

Sources & References

All claims and figures in this analysis are sourced from the publicly available references below.

1. American Bar Association M&A Committee Resources — ABA M&A Committee guidance on acquisition deal structures, sources & uses, debt and equity capital stack composition, and standard transaction documentation underlying LBO model construction.
2. U.S. Small Business Administration 7(a) Loan Program — SBA 7(a) loan program guidance on debt structuring for sub-$25M EV acquisitions including 10% buyer equity requirement, 10-year amortization, and other parameters relevant to acquisition model debt schedule construction.
3. Stanford Graduate School of Business 2024 Search Fund Study — Stanford GSB biennial Search Fund Study documenting search fund acquisition returns targets, MOIC and IRR benchmarks, and capital structure patterns relevant to search fund acquisition modeling.
4. Bain & Company Global Private Equity Report 2024 — Bain & Company analysis of LMM and middle-market PE returns, multiples, leverage trends, and benchmark IRR/MOIC outcomes across acquisition cycles relevant to model assumption calibration.
5. Ares Capital Corporation 10-K Filing (SEC EDGAR) — Ares Capital Corporation public filings disclosing direct lending portfolio composition, weighted-average yields, and standard LMM debt structures used in acquisition financing modeling.
6. PitchBook Private Equity Research — PitchBook industry data on private equity deal structures, leverage multiples, exit timing, and IRR/MOIC benchmarks across LMM and middle-market acquisitions used in acquisition model calibration.
7. GTCR Public Information — GTCR as middle-market private equity firm with documented platform-building strategies and add-on acquisition approaches relevant to roll-up acquisition model construction.
8. Carlyle Group Public Information — Carlyle Group (NASDAQ: CG) public filings disclosing $50B+ direct lending platform AUM and PE platform structures relevant to LMM acquisition financing and modeling assumptions.