Reverse-LBO in five minutes.

A reverse-LBO answers a question regular LBO models don't: at today's price, what's a private buyer actually willing to pay, and what does that tell us about the public valuation? If you read it correctly, it's one of the cleanest discipline tools on a public-equity desk. If you read it badly, it produces nonsense numbers that look authoritative. Five minutes to know which side you're on.

The setup

Forward LBO: pick an entry multiple, run the deal, see what IRR a sponsor would generate. Reverse LBO: pick a target IRR (call it 20%), hold the rest of the deal structure constant, and solve for the maximum entry multiple at which the sponsor still hits that hurdle. Everything else — exit multiple, leverage, hold period, operating path — is fixed; the entry multiple is the unknown.

Why is that useful? Because the implied entry multiple is your floor. Below that price, sponsors would underwrite the deal. Above it, public markets are pricing optimism that no financial buyer would put their LP money behind. That gap — or that overlap — is real information about the public valuation.

The math, briefly

Mechanically, you wrap the standard LBO engine in a bisection loop. Pick a starting bracket for entry multiple (say 3× to 25×). Run the forward LBO at the midpoint. If sponsor IRR > target, the entry can support a higher multiple, so move the lower bound up. If sponsor IRR < target, drop the upper bound. Iterate 50 times and you're converged to within rounding. The browser calculator on this site runs exactly this loop in JavaScript and gives you the answer in milliseconds.

Once you have the max entry multiple, the public-equity translation is mechanical: Implied take-private $/share = (max entry mult × EBITDA − net debt) / diluted shares. That's a real number you can compare to today's quote.

When to use it

Three contexts where reverse-LBO earns its keep, especially in cyclicals.

1. Pricing a public company against a hypothetical buyer. Imagine you're long a cyclical at $X. Bear case says the cycle peaks and the multiple compresses. Bull case says secular tailwinds put a floor under margins. Reverse-LBO at through-cycle EBITDA tells you: at what price would a sponsor underwrite this name at 20% IRR with 4.5× leverage? If the answer comes in above $X, the public is paying less than patient capital would — that's a real floor. If the answer comes in below $X, the public is paying for the bull case to persist; sponsors won't bid until the price falls or operating fundamentals improve.

2. Disciplining a bull thesis. Bull cases love multiple expansion. "If the market re-rates this from 8× to 12× on the transition story, the stock doubles." Fine. But would a sponsor ever pay 12× to buy this company? If the answer is "no sponsor would underwrite that even at 15% IRR," your bull case requires the public market to pay a multiple no rational private buyer would. That's not necessarily wrong — public markets pay re-rating premia all the time — but you should know it.

3. Take-private cases. When sponsor activity is high in a sector (energy circa 2020-2022, software through 2024), reverse- LBO is the natural framing for "is this name takeable?" Premium math directly off the implied entry multiple.

The five mistakes

Wrong leverage assumption. The single biggest source of reverse-LBO error. Most buyers won't lever a memory cyclical at 6×; most won't lever a software business at 4×. The whole answer shifts by 1.5x of multiple if you get this wrong. Use the leverage a lender would actually fund, not the leverage that makes your model work.

Forgetting the working capital and capex burn. In cyclicals, capex eats half of EBITDA in good years. NWC absorption grows with revenue. The reverse-LBO doesn't care about reported numbers; it cares about cash available to service debt. If your model treats capex as 5% of EBITDA on a memory name, you're wrong by a factor of three.

Picking exit multiple = entry multiple by reflex. The convention is fine for stable names, but for cyclicals it's a tell. If you're modeling MU at peak EBITDA in 2026 and assuming a flat 8× exit five years later, you've baked in that the cycle doesn't matter. Either model exit at mid-cycle EBITDA × mid-cycle multiple, or at peak EBITDA × compressed exit multiple. Don't double- count the cycle.

Confusing the proxy with bisection. Quick reverse- LBO formulas you see in pitches assume a flat debt-paydown schedule and a closed-form IRR. Real bisection on a real debt schedule with real cash sweep produces a different number, sometimes by 1-2 turns of multiple. The closed-form is fine for a back-of-envelope on the train. Don't bring it to an IC.

Reading too much into the $/share output. The implied take-private price is a methodology output, not a target. It's the price at which one specific sponsor with one specific target IRR would underwrite the deal. Different LP base, different cost of debt, different sector view — the answer changes. Don't tell yourself "the stock is going to $X because reverse-LBO says so." Tell yourself "below $X there's a credible floor; above $Y the public is paying more than patient capital."

What the framework can't do

Reverse-LBO doesn't predict transactions. Sponsors face availability of debt, regulatory friction, antitrust review, management cooperation, anti-takeover provisions, fiduciary-out language, and a dozen other constraints the math doesn't see. A name can clear reverse-LBO at 20% IRR and still never get a bid. Don't confuse "this is takeable" with "this will be taken." See the disclaimer for more.

It also doesn't tell you whether you should buy. The output is diagnostic: it tells you what kind of bet the public market is asking you to make. Is the public paying less than patient capital (a defensive position with a floor)? Is it paying more (a re-rating bet that depends on multiples expanding)? Either can be the right thesis. The reverse-LBO just tells you which one you're underwriting.

A worked example: MU

Run Micron through the browser calculator with the through-cycle scorecard: $13.8B mid-cycle EBITDA, 6% growth, 55% margin, 25% capex, 8× exit, 4.5× leverage, 20% target IRR, 5-year hold. The implied take-private price comes out around $85 against a current quote near $86 — essentially flat. The public is pricing the name exactly where a 20%-IRR sponsor running through- cycle would land.

Now flip to the AI-cycle scorecard: $18.2B EBITDA (peak-half method), 12% growth, 58% margin, 32% capex, 10× exit, 4.0× leverage, 20% target IRR. Implied take-private now lands much higher, around $146. The spread between the two scorecards — that delta between $85 and $146 — is the option value of your structural-vs-cyclical call on memory. If you believe HBM structurally lifts the floor, you're in the AI-cycle world. If you think margins revert when supply catches up, through-cycle is the honest reading. That ~$60 of spread is your call to make.

The full workbook with both pulls and the audit trail is at the Reverse-LBO Template page. The MU through-cycle long covering the same name from a public-equity angle is at mu-through-cycle.html; the new "If a sponsor took this private" appendix on that page reads directly off this framework.

The five-minute version, in one paragraph

Reverse-LBO solves an LBO model backwards: fix the target IRR, vary the entry multiple, find the price a sponsor would underwrite. Convert to dollars per share by netting net debt and dividing by diluted shares. Compare to the current quote. If implied price is above current, the public is paying less than private capital would — a floor. If below, the public is paying for an exit multiple no sponsor would underwrite. Use it to discipline bull theses and ground public-market analysis in capital-structure reality. Don't use it to predict take-privates — sponsors face frictions the math doesn't see.

Brandon Leon writes independent equity research focused on cyclicals. The full disclaimer covers everything in detail at disclaimer. Spotted an error or have a better example? Email [email protected].