> ## Documentation Index > Fetch the complete documentation index at: https://financecontext.com/llms.txt > Use this file to discover all available pages before exploring further. # LBO Model Skill > Learn leveraged buyouts from scratch -- what an LBO is, how debt creates returns, the LBO math (entry, operations, exit), IRR/MOIC, and how to build institutional-quality LBO models. # LBO Model Skill ## What is a Leveraged Buyout? A leveraged buyout (LBO) is a transaction where a private equity firm acquires a company using a combination of equity (the PE firm's own money) and a significant amount of debt (borrowed money). The word "leveraged" refers to the use of debt as a lever to amplify returns -- just as a physical lever lets you move a heavy object with less force, financial leverage lets you control a large asset with a relatively small equity investment. Here is a simplified example: Imagine you buy a house for $500,000. You put down $100,000 (20%) and borrow $400,000 (80%). Five years later, you sell the house for $700,000. After repaying the $400,000 loan, you have $300,000 -- a 3x return on your original $100,000 investment. If you had paid all cash ($500,000), you would have made $200,000 -- only a 1.4x return. The debt amplified your returns because you controlled a $500,000 asset with only \$100,000 of your own money. LBOs work the same way at a corporate scale. A PE firm might buy a $1 billion company with $400M of equity and $600M of debt. Over 5 years, the company uses its cash flows to pay down the debt. At exit, the company might sell for $1.5 billion. After repaying the remaining debt (say $300M after paydowns), the PE firm receives $1.2 billion on its \$400M investment -- a 3x return. **Quick numeric example:** A PE firm acquires TargetCo for $800M using $320M of equity and $480M of debt (6.0x leverage on $80M EBITDA). Over 5 years, EBITDA grows to $110M and the company pays down $180M of debt. At exit at 10.0x EBITDA, the exit enterprise value is $1,100M. After repaying $300M of remaining debt, equity proceeds are $800M on a $320M investment -- a **2.5x MOIC** and approximately **20% IRR**. The LBO model is the analytical tool that evaluates whether this math works for a specific deal. It projects the company's cash flows, models the debt paydown, and calculates the expected returns (IRR and MOIC) under various scenarios. ## Detailed Worked Example Let us build a complete LBO model for **IndustrialParts Inc.**, a fictional manufacturer with stable cash flows. **Assumptions:** * LTM EBITDA: \$100M * Entry Multiple: 8.0x (Purchase Price = \$800M) * Total Debt: 5.0x EBITDA = \$500M * Sponsor Equity: $300M (the "plug" -- $800M purchase price minus \$500M debt) * Transaction fees: \$20M (advisory + financing) * Total Uses: $820M; Total Sources: $500M debt + \$320M equity * Revenue: \$500M growing at 5% per year * EBITDA margin: 20% (stable) * D\&A: 3% of revenue * CapEx: 4% of revenue * Change in NWC: 1% of incremental revenue * Tax rate: 25% * Interest rate: 6% on all debt (simplified) * Hold period: 5 years * Exit Multiple: 8.0x (no multiple expansion) **Uses of Funds:** ``` Enterprise Value (8.0x x $100M) = $800.0M Transaction Fees (advisory + legal) = $20.0M Total Uses = $820.0M ``` **Sources of Funds:** ``` Senior Secured Debt (3.5x EBITDA) = $350.0M Subordinated Debt (1.5x EBITDA) = $150.0M Total Debt = $500.0M Sponsor Equity (plug) = $320.0M Total Sources = $820.0M ``` **Check:** Sources ($820M) = Uses ($820M). The equity check is $320M, not $300M, because the sponsor also funds the \$20M of transaction fees. | Year | Revenue | Growth | EBITDA (20%) | D\&A (3%) | EBIT | | ------- | -------- | ------ | ------------ | --------- | -------- | | 0 (LTM) | \$500.0M | -- | \$100.0M | \$15.0M | \$85.0M | | 1 | \$525.0M | 5.0% | \$105.0M | \$15.8M | \$89.3M | | 2 | \$551.3M | 5.0% | \$110.3M | \$16.5M | \$93.7M | | 3 | \$578.8M | 5.0% | \$115.8M | \$17.4M | \$98.4M | | 4 | \$607.8M | 5.0% | \$121.6M | \$18.2M | \$103.3M | | 5 | \$638.1M | 5.0% | \$127.6M | \$19.1M | \$108.5M | For each year, calculate the cash available for debt service: ``` Year 1: EBIT = $89.3M (-) Taxes (25%) = $22.3M = NOPAT = $67.0M (+) D&A = $15.8M (-) CapEx (4% of revenue) = $21.0M (-) Change in NWC (1% x dRev) = $0.3M (-) Interest ($500M x 6%) = $30.0M = Levered FCF (for debt repay) = $31.5M ``` Full schedule: | Year | NOPAT | D\&A | CapEx | dNWC | Interest | Levered FCF | | ---- | ------- | ------- | ------- | ------ | -------- | ----------- | | 1 | \$67.0M | \$15.8M | \$21.0M | \$0.3M | \$30.0M | \$31.5M | | 2 | \$70.3M | \$16.5M | \$22.1M | \$0.3M | \$28.1M | \$36.4M | | 3 | \$73.8M | \$17.4M | \$23.2M | \$0.3M | \$25.9M | \$41.8M | | 4 | \$77.5M | \$18.2M | \$24.3M | \$0.3M | \$23.4M | \$47.8M | | 5 | \$81.4M | \$19.1M | \$25.5M | \$0.3M | \$20.5M | \$54.2M | Note: Interest declines each year as debt is paid down. Interest is calculated on the beginning-of-year debt balance to avoid circular references. Debt is paid down using levered free cash flow. Senior debt is repaid first. | Year | Beg Debt | Interest | Repayment | End Debt | Leverage (Debt/EBITDA) | | ---- | -------- | -------- | --------- | -------- | ---------------------- | | 0 | -- | -- | -- | \$500.0M | 5.0x | | 1 | \$500.0M | \$30.0M | \$31.5M | \$468.5M | 4.5x | | 2 | \$468.5M | \$28.1M | \$36.4M | \$432.1M | 3.9x | | 3 | \$432.1M | \$25.9M | \$41.8M | \$390.3M | 3.4x | | 4 | \$390.3M | \$23.4M | \$47.8M | \$342.5M | 2.8x | | 5 | \$342.5M | \$20.5M | \$54.2M | \$288.3M | 2.3x | Over 5 years, the sponsor pays down $211.7M of debt ($500M to \$288.3M). The company deleverages from 5.0x to 2.3x EBITDA. ``` Exit EBITDA (Year 5) = $127.6M Exit Multiple = 8.0x Exit Enterprise Value = $1,021.1M (-) Remaining Debt = $288.3M = Exit Equity Proceeds = $732.8M MOIC = $732.8M / $320.0M = 2.29x IRR (5-year holding period) = ~18.0% ``` **Returns Attribution:** ``` Total value created: $732.8M - $320.0M = $412.8M From EBITDA Growth: ($127.6M - $100.0M) x 8.0x = $221.1M (54% of value) From Debt Paydown: $500.0M - $288.3M = $211.7M (51% of value) From Multiple Expansion: ($0) x $127.6M = $0 (0% of value) Note: Percentages exceed 100% because equity includes fees funded at entry ($20M), which offsets returns. ``` This deal generates approximately 18% IRR -- just below the typical 20% hurdle rate. The sponsor would need either a higher exit multiple, faster EBITDA growth, or a lower entry price to clear the hurdle. Build a 5x5 sensitivity table showing IRR at different entry and exit multiples: | Entry \ Exit | 7.0x | 7.5x | 8.0x | 8.5x | 9.0x | | ------------ | ----- | ----- | --------- | ----- | ----- | | **7.0x** | 18.5% | 21.0% | 23.3% | 25.5% | 27.5% | | **7.5x** | 15.8% | 18.2% | 20.5% | 22.6% | 24.6% | | **8.0x** | 13.4% | 15.7% | **18.0%** | 20.1% | 22.0% | | **8.5x** | 11.2% | 13.5% | 15.6% | 17.7% | 19.6% | | **9.0x** | 9.2% | 11.4% | 13.5% | 15.5% | 17.4% | The center cell (8.0x entry / 8.0x exit) should match the base case IRR of approximately 18.0%. The table shows that even 0.5x of multiple expansion (entry 8.0x, exit 8.5x) adds approximately 2 percentage points to IRR. ## Why It Matters LBO analysis is central to private equity but extends well beyond PE firms: * **PE firms** use LBO models to evaluate every potential acquisition. The model determines the maximum price they can pay while still achieving their target returns (typically 20%+ IRR). * **Investment bankers** build LBO models for sell-side advisory to understand what PE firms would be willing to pay, and for buy-side advisory to help PE clients structure deals. * **Leveraged finance teams** use LBO models to evaluate creditworthiness and structure the debt packages that fund these transactions. * **Corporate development teams** use LBO analysis to understand whether a PE firm might bid on a division they are considering divesting. If you do LBO analysis poorly, you might overpay for an acquisition (destroying value for your LPs), or you might structure debt incorrectly (creating financial distress for the portfolio company). ## Key Concepts | Term | Definition | Why It Matters | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | | **IRR** | Internal Rate of Return. The annualized return on the equity investment, accounting for the timing of cash flows. | The primary return metric PE firms use. A deal needs to clear the firm's hurdle rate (typically 20%+) to proceed. | | **MOIC** | Multiple of Invested Capital. Total proceeds divided by total equity invested. A 3.0x MOIC means you tripled your money. | Simpler than IRR -- does not account for timing. A 3.0x MOIC over 3 years is much better than 3.0x over 10 years. | | **Sources & Uses** | The financing structure of the transaction. Sources = where the money comes from (equity + debt). Uses = where the money goes (purchase price + fees). Sources must equal Uses. | The foundation of the LBO model. Getting this wrong means everything downstream is wrong. | | **Entry Multiple** | The EV/EBITDA multiple at which the company is acquired. Entry EV = Entry Multiple x LTM EBITDA. | Determines the purchase price. Lower entry multiples mean more potential upside. | | **Exit Multiple** | The EV/EBITDA multiple at which the company is sold at the end of the hold period. | The single biggest driver of returns in most LBOs. Even 0.5x of multiple expansion can add 5%+ to IRR. | | **Leverage** | Total Debt / EBITDA. Typical range: 4x-6x for most LBOs. | Higher leverage amplifies returns but also increases risk. Too much debt can cause financial distress. | | **Cash Sweep** | Excess cash flow used to pay down debt, following a priority waterfall (senior debt first). | Debt paydown is one of the three sources of LBO returns (along with EBITDA growth and multiple expansion). | | **Debt Tranches** | Different layers of debt with different seniority, rates, and terms (Senior Secured, Senior Unsecured, Mezzanine). | Senior debt is cheaper but has tighter covenants. Subordinated debt is more expensive but more flexible. | | **PIK Interest** | Payment-in-Kind interest that accrues to the principal balance instead of being paid in cash. | Preserves cash flow for operations but increases the debt balance over time. Common in mezzanine financing. | | **Management Rollover** | When the target company's management team reinvests a portion of their equity into the new deal. | Aligns management incentives with the sponsor. Must be modeled because it dilutes the sponsor's share of exit proceeds. | ## How It Works **Triggers when:** completing LBO model templates, deal materials requiring LBO analysis, or investment committee presentations for PE acquisitions. **Formulas over hardcodes is non-negotiable.** Every calculation must be an Excel formula. When using openpyxl, write `cell.value = "=B5*B6"` (formula string), NOT `cell.value = 1250` (computed result). The model must be dynamic and update when inputs change. ### The Three Sources of LBO Returns Understanding this framework is essential. PE returns come from three sources: 1. **EBITDA Growth** -- Growing the company's earnings through revenue growth, margin improvement, or cost cuts. 2. **Debt Paydown** -- Using the company's cash flows to pay down debt, increasing the equity owner's share of enterprise value. 3. **Multiple Expansion** -- Selling the company at a higher EV/EBITDA multiple than you bought it at (e.g., buy at 8x, sell at 10x). The best deals have all three working. The riskiest deals depend primarily on multiple expansion (which you cannot control). ### Environment: Office JS vs Python/openpyxl Use Office JS directly. Write formulas via `range.formulas = [["=B5*B6"]]`. No separate recalc step needed; Excel calculates natively. Use `range.format.*` for styling. **Merged cell pitfall:** Do NOT call `.merge()` then set `.values` on the merged range -- it throws `InvalidArgument`. Instead write value to the top-left cell alone, then merge and format: ```js theme={null} ws.getRange("A7").values = [["SOURCES & USES"]]; const hdr = ws.getRange("A7:F7"); hdr.merge(); hdr.format.fill.color = "#1F4E79"; hdr.format.font.bold = true; hdr.format.font.color = "#FFFFFF"; ``` Use Python/openpyxl. Write formula strings (`ws["D20"] = "=B5*B6"`), then run `recalc.py` before delivery. Follow the same color conventions: blue font for inputs, black for formulas, purple for same-tab links, green for cross-tab links. ### Model Structure The transaction financing structure. Sources must equal Uses -- this is the fundamental balancing equation. * **Sources:** Equity contribution, Senior Secured, Senior Unsecured, Mezz/Sub debt * **Uses:** Equity purchase price, transaction fees, financing fees, balance sheet cash One item is always the "plug" (balancing figure) -- typically equity contribution (how much the PE firm needs to write the check for). **Formula pattern:** ``` Sponsor Equity = Total Uses - Total Debt Sources ``` Revenue build, cost structure, and EBITDA through the projection period (typically 5 years). * Revenue growth by year (Bear/Base/Bull scenarios) * Gross margin and EBITDA margin progression * D\&A schedule linked to PP\&E * Working capital assumptions * Tax calculation: EBIT x Tax Rate (handle negative EBIT with MAX function) Beginning and ending balances for each debt tranche, interest calculations, and cash sweep mechanics. * Interest calculated on beginning balances (to avoid circularity) * Cash sweep respects payment priority waterfall (senior first) * Balances cannot go negative (MAX/MIN functions) * PIK interest accrues to principal: Ending Balance = Beginning + PIK - Cash Paydown **Formula pattern for mandatory amortization:** ``` Mandatory Repayment = MIN(Scheduled Amount, Beginning Balance) ``` **Formula pattern for cash sweep:** ``` Available for Sweep = MAX(0, Levered FCF - Mandatory Repayments) Senior Sweep = MIN(Available for Sweep, Senior Beginning Balance - Senior Mandatory) Sub Sweep = MIN(Available - Senior Sweep, Sub Beginning Balance - Sub Mandatory) ``` Exit valuation, equity proceeds distribution, and IRR/MOIC calculation. * Exit EV = Exit EBITDA x Exit Multiple * Equity proceeds = Exit EV - Net Debt at exit * IRR and MOIC calculated on sponsor cash flows * Sensitivity tables: IRR/MOIC across entry multiple vs. exit multiple (5x5 or 7x7 grids) **IRR cash flow setup:** ``` Year 0: -$320M (equity invested -- MUST be negative) Year 1-4: $0 (no interim distributions in most LBOs) Year 5: +$732.8M (exit equity proceeds -- MUST be positive) ``` ### Step-by-Step User Verification Do NOT build the entire model end-to-end. Verify with the user at each checkpoint: Show the balanced table. Confirm the plug is correct and sources = uses. Get sign-off before building the operating model. Show the projected P\&L. Confirm growth rates and margins look right. Get sign-off before the debt schedule. Show beginning/ending balances and interest. Confirm the waterfall logic. Get sign-off before returns. Show the cash flow series and outputs. Confirm signs and ranges. Get sign-off before sensitivity tables. Verify center cell = base case IRR/MOIC. Confirm all values vary as expected and move in the right directions. ### Verification Checklist * Sources = Uses (balances exactly) * Assets = Liabilities + Equity (balance sheet, if included) * CF Ending Cash = BS Cash (every period) * Interest calculated on beginning balances * Debt balances cannot go negative * IRR cash flow signs are correct (investment = negative, proceeds = positive) * Sensitivity table center cell = base case IRR/MOIC * All hardcoded inputs in blue, formulas in black ### Formatting Standards **Fill Color Palette (Professional Blues and Greys):** | Element | Fill Color | Font | | ----------------------- | ----------------------------- | ---------------- | | Section headers | Dark blue `#1F4E79` | White bold | | Column headers | Light blue `#D9E1F2` | Black bold | | Input cells | Light grey `#F2F2F2` or white | Blue `#0000FF` | | Formula cells | White | Black | | Same-tab links | White | Purple `#800080` | | Cross-tab links | White | Green `#008000` | | Key outputs (IRR, MOIC) | Medium blue `#BDD7EE` | Black bold | **Number Formatting:** * Currency: `$#,##0;($#,##0)` or `$#,##0.0` * Percentages: `0.0%` * Multiples: `0.0"x"` * MOIC: `0.00"x"` (two decimals for precision) * All numeric cells right-aligned ### Formula Recalculation Run `python recalc.py model.xlsx 30` before delivery. Fix ALL errors until status is "success." Zero formula errors required. ## Common Mistakes **The mistake:** Calculating interest on the average of beginning and ending debt balances, creating a circular reference because ending balance depends on cash flow, which depends on interest. **Why it happens:** Average balance is technically more accurate. But in Excel without iterative calculation enabled, it creates an unresolvable loop. **The fix:** Calculate interest on beginning-of-year balances. The small accuracy loss is irrelevant compared to the debugging nightmare of circular references. Pattern: Interest = Beginning Balance x Rate. This breaks the loop because beginning balance is known from the prior period. **The mistake:** Setting the initial equity investment as positive and exit proceeds as negative (or all positive), producing a nonsensical IRR. **Why it happens:** Confusion about whose perspective the cash flows represent. From the sponsor's perspective, the initial investment is cash going out (negative) and exit proceeds are cash coming in (positive). **The fix:** Year 0 cash flow = negative equity invested. Final year cash flow = positive exit equity proceeds. If using XIRR, ensure dates are also correct and sequential. **The mistake:** Setting sponsor equity = purchase price minus debt, ignoring that advisory fees, financing fees, and legal costs also need to be funded. **Why it happens:** The fees seem small relative to the deal size and are easy to forget. **The fix:** Total Uses = Purchase Price + Advisory Fees + Financing Fees + Legal Costs. Sponsor Equity = Total Uses - Total Debt. Even $20M of fees on an $800M deal increases sponsor equity by 6%, which directly reduces MOIC and IRR. **The mistake:** Applying the exit multiple to Year 4 EBITDA instead of Year 5, or to NTM EBITDA instead of LTM. **Why it happens:** Confusion about whether the exit happens at the beginning or end of Year 5, and whether the buyer pays on trailing or forward earnings. **The fix:** Be explicit about the convention. Most LBO models apply the exit multiple to the final projection year's EBITDA (Year 5 if 5-year hold). If your firm uses NTM, calculate Year 6 EBITDA and apply the multiple to that. Document the convention in a cell comment. **The mistake:** Calculating returns as if the sponsor owns 100% of the equity when management rolled over 10-20% of their equity into the new deal. **Why it happens:** The rollover is mentioned in the deal terms but not modeled. It feels like a detail rather than a driver. **The fix:** If management rolls over $30M of their $50M equity stake, then exit equity proceeds must be split: management gets their pro rata share (e.g., 30/320 = 9.4% of exit equity). The sponsor's returns are calculated only on their share. Ignoring rollover overstates sponsor MOIC and IRR. **The mistake:** The debt schedule shows a negative ending balance because the cash sweep repaid more than the outstanding balance. **Why it happens:** The sweep formula does not cap repayment at the remaining balance. **The fix:** Use MIN functions: `Repayment = MIN(Available Cash, Remaining Debt Balance)`. This ensures the balance floors at zero. Without this guard, the model may show negative debt (the company lending money to the bank), which is nonsensical. **The mistake:** Computing revenue x margin in Python and writing the EBITDA number to the cell instead of writing the formula string. **Why it happens:** It feels faster, and the number is "right" at the time of writing. **The fix:** Every derived cell must contain a formula. Write `ws["C15"] = "=C10*C12"`, not `ws["C15"] = 105.0`. A hardcoded model cannot be flexed when the MD asks "what if EBITDA margins improve by 200bps?" -- every cell needs manual recalculation. **The mistake:** Building the model only for the base case and not testing whether the company can survive a downside scenario with the proposed debt load. **Why it happens:** The base case shows attractive returns and the team is eager to move forward. **The fix:** Run the model with bear case assumptions (lower growth, compressed margins). Check: Does the company maintain positive cash flow? Does leverage stay below covenant limits? Can it service debt even in a downturn? If the bear case triggers default, the debt structure is too aggressive. **The mistake:** Every cell in the sensitivity table shows the same IRR because the formulas all reference the same fixed cells instead of the varying row/column headers. **Why it happens:** The formula was written with absolute references ($B$5) where it needed mixed references ($A5 for the row input, B$4 for the column input). **The fix:** Use mixed references in sensitivity formulas. The row axis value should use an absolute column reference ($A5), and the column axis value should use an absolute row reference (B$4). Each cell should produce a different output. If all outputs are identical, the formula is not actually varying. **The mistake:** Using unlevered FCF for debt repayment, or using levered FCF for the DCF valuation portion of the model. **Why it happens:** Both are called "free cash flow" and the distinction is easy to miss. **The fix:** In an LBO model, you need levered FCF (after interest expense) to determine how much cash is available for debt service. Unlevered FCF (before interest) is used in DCF valuation. Label your FCF line items clearly: "Unlevered FCF" vs. "Levered FCF" or "Cash Available for Debt Service." ## Daily Workflow Your fund is evaluating a $500M acquisition of a specialty chemicals company with $65M of EBITDA. The deal team needs to determine the maximum entry price that still delivers 20%+ IRR. **Workflow:** 1. Gather the target's historical financials from the CIM (revenue, EBITDA, margins, CapEx, working capital) 2. Build the Sources & Uses with the proposed debt structure (check with the leveraged finance team for indicative terms) 3. Project revenue and EBITDA for 5 years using management's projections as the base case 4. Build the debt schedule with mandatory amortization and a cash sweep 5. Calculate exit proceeds at various exit multiples (7.0x to 10.0x) 6. Build a sensitivity table: Entry Multiple (6.0x to 9.0x) vs. Exit Multiple (7.0x to 10.0x) 7. Find the maximum entry multiple where the base case IRR still exceeds 20% 8. Present to the investment committee with the sensitivity table, returns attribution, and downside stress test You are advising a founder-owned business on a sale process. Several PE firms have expressed interest, and the MD wants to understand what PE can afford to pay. **Workflow:** 1. Build an LBO model using the seller's financial projections 2. Use market-standard debt terms (check recent leveraged finance transactions for comparable companies) 3. Calculate the maximum purchase price at which a PE firm can achieve 20% IRR and 2.5x MOIC 4. Run scenarios: What if the buyer achieves 200bps of margin improvement through cost cuts? What if they can exit at a higher multiple due to organic growth? 5. Present a "PE affordability analysis" showing the price range PE firms are likely to bid ($400M-$550M) 6. Compare this to strategic buyer valuations (typically higher because strategics have synergies) 7. Advise the client on whether to run a dual-track process (PE + strategic) Your fund's portfolio company (a healthcare services platform) wants to acquire a smaller competitor for \$50M. You need to model the add-on to determine if it enhances or dilutes the platform's returns. **Workflow:** 1. Build a standalone LBO for the add-on target using the platform's existing credit facility terms 2. Model the operating synergies: \$3M of cost savings from eliminating duplicate corporate functions 3. Build a pro forma combined model showing the platform's new consolidated EBITDA 4. Calculate the new blended entry multiple and required exit multiple to maintain the fund's target returns 5. Compare: Does the add-on create value by expanding EBITDA at a lower multiple than the platform's entry price? 6. Present the analysis with clear recommendation: proceed at $50M, negotiate down to $45M, or walk away ## Practice Exercise **Scenario:** A PE fund is evaluating the acquisition of FoodService Corp, a commercial food distribution company. **Given Information:** * LTM Revenue: \$400M, growing at 6% per year * LTM EBITDA: \$60M (15% margin, expected to expand to 17% by Year 3 through procurement savings) * D\&A: 2.5% of revenue; CapEx: 3.5% of revenue * Change in NWC: 1.5% of incremental revenue * Tax rate: 25% * Entry Multiple: 7.5x EBITDA (\$450M purchase price) * Debt Structure: Senior Secured at 4.0x EBITDA ($240M) at SOFR + 350bps (assume 8.5% all-in); Subordinated at 1.5x EBITDA ($90M) at 10.0% fixed * Transaction fees: \$15M * Hold period: 5 years * Exit Multiple: 8.0x (modest 0.5x expansion due to improved margins and scale) **Your tasks:** 1. Build the Sources & Uses table (calculate sponsor equity as the plug) 2. Project revenue and EBITDA for 5 years (remember margin expansion from 15% to 17% by Year 3) 3. Calculate levered FCF for each year (account for different interest rates on each tranche) 4. Build the debt schedule with senior debt repaid first via cash sweep 5. Calculate exit proceeds, MOIC, and IRR 6. Build a 5x5 sensitivity table: Exit Multiple (6.5x to 8.5x) vs. EBITDA Margin at Exit (15% to 19%) 7. Perform returns attribution: how much comes from EBITDA growth, debt paydown, and multiple expansion? 8. Stress test: What happens if revenue growth drops to 3% and margins stay flat at 15%? The sponsor equity check should be $450M + $15M - $240M - $90M = $135M. With $60M of EBITDA growing at 6% and expanding margins, Year 5 EBITDA should be approximately $91M. At 8.0x exit, EV = $728M. This is a strong setup for 20%+ returns -- but verify the debt service coverage in the early years when margins are still at 15%. ## How to Add to Your Local Context ```bash theme={null} claude plugin install financial-analysis@financial-services-plugins ``` Edit `skills/lbo-model/SKILL.md`: ```markdown theme={null} ## Firm-Specific LBO Conventions ### Debt Structure Defaults - Senior Secured: 4.0x EBITDA at SOFR + 350bps - Subordinated: 1.5x EBITDA at 8.5% fixed - Never model mezzanine above 2.0x without explicit approval ### Returns Thresholds - Minimum target IRR: 20% gross - Target MOIC: 2.5x or higher - Flag deals where > 50% of returns come from multiple expansion ``` If your firm has a standard LBO template: ```markdown theme={null} ## Template - Always use our standard LBO template (stored in Egnyte at /templates/LBO_Template_v4.xlsx) - Template uses positive convention for revenues and EBITDA - Cash flow items follow IB standard sign convention ``` Edit `.mcp.json` to point to your preferred data providers: ```json theme={null} { "mcpServers": { "factset": { "url": "https://mcp.factset.com/mcp", "headers": { "Authorization": "Bearer YOUR_FACTSET_API_KEY" } } } } ``` ## Common Pitfalls These are the most frequent sources of error in LBO models: * **Circular references from interest** -- Interest depends on debt balance, which depends on cash flow, which depends on interest. Break the circle by calculating interest on beginning balances, not average balances. * **Wrong cash flow signs for IRR** -- The initial investment must be negative, exit proceeds must be positive. Reversed signs produce nonsensical IRR. * **Exit multiple applied to wrong EBITDA** -- Some models use LTM exit EBITDA, others use NTM. Be explicit about which convention you are using. * **Ignoring management rollover** -- If management rolls over equity, it dilutes the sponsor's returns. This must be modeled. * **Transaction fees missing from Uses** -- Advisory fees, financing fees, and legal costs reduce day-1 equity and must be included. * **Debt paydown exceeding available cash** -- Use MAX(0, ...) functions to prevent debt balances from going negative.