Comparable Company Analysis

Comparable company analysis — "comps" — estimates a company's value by observing what the market currently pays for similar businesses. You take a set of peers, calculate their valuation multiples (EV/EBITDA, P/E, EV/Revenue, P/FCF), then apply that multiple range to your subject company's financial metrics to derive an implied price range. Comps are the standard sanity check alongside a DCF model: the DCF tells you what the business is worth on its own merits; comps tell you where the market is pricing the category. When they diverge significantly, you need a reason. See stock valuation methods compared for when each approach dominates.

The peer group is the most consequential decision in a comps analysis. An analyst who cherry-picks peers gets any answer they want, so the selection criteria must be defined before you start screening — not after you see which companies produce flattering multiples.

Four concrete filters in order of strictness:

Hard exclusions, no exceptions: companies in active M&A (target or acquirer) and companies in financial distress (interest coverage below 1.5x or covenant violations disclosed). M&A targets trade at a control premium that has nothing to do with standalone value. Distressed companies trade on recovery value, not earnings power.

Aim for 5–10 peers. Fewer than 5 gives you a percentile range with no statistical meaning. More than 12 usually means you have relaxed the criteria until the peer group is so heterogeneous it tells you nothing.

The multiple you choose should be determined by the business type, not by which one produces the most convenient answer. Each multiple has a specific structural reason it works well for certain companies and lies for others.

EV/EBITDA — capital-intensive mature businesses

EV/EBITDA is the workhorse multiple for M&A and for comparing businesses across different capital structures. Because it uses enterprise value (market cap plus net debt) in the numerator and strips interest, taxes, and D&A from the denominator, it neutralizes three sources of noise: financing choices, tax jurisdiction, and accounting depreciation schedules. Use EV/EBITDA for manufacturers, cable companies, utilities, and any business where D&A differences across peers are large enough to distort a P/E comparison.

P/E — mature earners with stable earnings

Price-to-earnings works well when peers have similar leverage, similar tax rates, and earnings that are genuinely representative. P/E breaks down when you are comparing a levered company against an unlevered peer. Normalize earnings before comparing — strip asset sale gains, restructuring charges, and any “adjusted” add-backs that conveniently show up every quarter.

EV/Revenue — high-growth or pre-profit companies

When a company is unprofitable or in the early phases of scaling, EBITDA and earnings are either negative or too small to anchor a meaningful multiple. EV/Revenue sidesteps this by using the top line. The tradeoff: revenue multiples embed an implicit assumption about future margins. Flag the implied margin expectation explicitly.

P/FCF — capex-light models

Price-to-free-cash-flow is the cleanest multiple for businesses where CapEx is minimal and earnings convert directly to cash — SaaS companies, marketplace platforms, asset-light franchisors. For software or platform businesses where peers have vastly different D&A loads from acquisitions, P/FCF often gives a cleaner cross-company comparison than P/E.

Multiple selection decision tree:

1. Is the subject company profitable on an EBITDA basis? If no → use EV/Revenue.
2. Does the peer group have materially different leverage levels or D&A schedules? If yes → use EV/EBITDA, not P/E.
3. Is CapEx under 5% of revenue for most peers? If yes → consider adding P/FCF alongside EV/EBITDA.
4. Are earnings stable and leverage uniform across peers? If yes → P/E is acceptable as the primary multiple.
5. Use at least two multiples and note when they disagree — the disagreement is information.

A comps table is only as useful as the consistency of its inputs. The two most common ways analysts corrupt their own comps: mixing LTM and NTM figures within the same column, and carrying through one-time items that distort the denominator.

LTM vs NTM: Last-twelve-months (LTM) multiples use reported financials and are directly verifiable from filings. Next-twelve-months (NTM) multiples use consensus estimates and forward-price the stock against expected performance. Both are valid — but you must use one consistently across every peer in the table. A mixed table where you use LTM EBITDA for companies that have already reported Q1 and NTM EBITDA for those that haven't is comparing different time horizons.

Stripping one-time items: For every peer, adjust the denominator to remove items that will not recur — restructuring charges, gain/loss on asset sales, impairments, acquisition-related amortization, and COVID-era anomalies for businesses that had unusual years.

Comps table construction checklist:

1. Pull LTM financials from the most recent 10-K/10-Q for all peers. Calculate NTM from consensus if needed — document the source and date.
2. Adjust each peer's EBITDA/EPS/FCF for identified one-time items. Note every adjustment in a footnote.
3. Calculate EV for each peer: market cap + total debt − cash and equivalents. Use fully diluted shares.
4. Compute multiples for each peer. Flag outliers (multiples more than 2 standard deviations from the group).
5. Calculate 25th percentile, median, and 75th percentile across the peer group. Do not report just the mean.

The 25th/median/75th percentile structure is important: it brackets the peer distribution without being distorted by outliers, and it forces the analysis to acknowledge that your subject company's appropriate multiple depends on where it ranks within the peer group.

Apply the 25th percentile multiple to your subject company's metrics for the low end of the range, and the 75th percentile for the high end. The median is your central estimate. This produces an implied enterprise value range; convert to equity value by subtracting net debt, then divide by fully diluted shares to get an implied price range.

The output should always be a two-column table: low and high. A single-point estimate from comps is false precision — the spread in peer multiples already tells you the market assigns a wide range of values to businesses in this subsector.

When the comps-derived range and your DCF range overlap, the thesis is more robust. When they diverge by more than 30%, you are either modeling a different set of assumptions than the market is or your peer group has a structural issue. Triangulate rather than average — understand why the methods disagree.

Comps are the valuation method most susceptible to motivated reasoning, because the answer is directly determined by which peers you pick and which time period you use.