Peer Analysis Done Right: How to Select True Comparable Companies

Pull up the comp table in almost any analyst report and look hard at who made the list. In my experience, a few of the names usually don't belong. They share an industry code with the target, or the analyst happens to cover them, but they don't resemble the business being valued in any way that matters. And because relative valuation only works when the comparison set is honest, a sloppy peer group quietly corrupts every multiple, premium, and discount you derive from it.

Selecting comparables well is a judgment exercise. You need to understand how each candidate makes money, how fast it's growing, what its margins look like, and what its balance sheet is carrying. An industry classification gives you a starting universe and nothing more. Below is the process I use, the places I go to find candidates, and the mistakes I see most often.

Why Industry Classifications Alone Will Mislead You

GICS, SIC codes, and NAICS codes group companies by industry, and they're genuinely useful for pulling an initial list. The trouble is that they routinely lump together companies with completely different economics.

Take the technology sector. Amazon, Microsoft, Salesforce, and NVIDIA all get grouped under tech in casual conversation and in plenty of comp tables. But Amazon is mostly a retailer with a large cloud business attached. Microsoft sells enterprise software and cloud infrastructure. Salesforce sells subscription CRM. NVIDIA designs semiconductors. Comparing their P/E or EV/EBITDA multiples head to head makes about as much sense as comparing the fuel efficiency of a bicycle and a tractor because both are technically vehicles.

The problem doesn't go away inside narrower sub-industries either. Two companies classified as application software can run opposite models. One sells perpetual licenses with maintenance contracts, the other sells monthly subscriptions. Revenue recognition, growth cadence, margin structure, and customer economics all diverge, so a direct multiple comparison between the two tells you very little.

The Five Dimensions of Comparability

When I'm deciding whether a candidate belongs in a peer group, I check it against five dimensions.

1. Business model

This is the one I weight most heavily. Comparable companies should make money in fundamentally the same way. A subscription software company belongs next to other subscription software companies, and a hardware vendor or systems integrator serving the same end market doesn't qualify.

Business model determines revenue quality (recurring versus transactional), margin structure (high gross margin software versus lower margin services), capital intensity, and how growth actually happens (product-led expansion versus an expensive sales motion). Those factors explain valuation multiples far better than an industry label does.

2. Size

Companies of different sizes face different constraints. A large company in an established market gets valued differently than a small one in the same market, even at similar growth rates, because the large one has scale advantages and a harder time compounding off a big base, while the small one has runway but carries execution risk the large one already retired. As a working rule, I try to keep peers within roughly three to five times the target's revenue or market cap. Put a $1 billion company next to a $100 billion company and scale distortions swamp whatever signal you were hoping to extract.

3. Growth rate and trajectory

Growth is the biggest driver of forward multiples, so peers should be growing at broadly similar rates. A company compounding revenue at 30% shouldn't be benchmarked against 5% growers just because they share a sub-industry.

Match on the trajectory as well as the current number. Say two companies are both growing 25% today, but one decelerated from 40% and the other accelerated from 15%. The market treats those two very differently, and it should, because deceleration and acceleration imply different futures.

4. Margin profile

Gross margin deserves particular attention because it reflects the underlying economics of the product. Two software companies can post very different operating margins simply because one is spending aggressively on sales while the other is harvesting, but if they sell comparable products, their gross margins should land in the same neighborhood. When gross margins diverge widely, that usually means the business models differ more than the classification suggests. Either drop the outlier or figure out exactly why before keeping it.

5. Capital structure and asset intensity

Leverage amplifies equity returns in both directions, so a heavily levered company trades at different equity multiples than an unlevered one even when operating performance is identical. Enterprise value multiples neutralize some of that, but not all of it, and asset intensity matters on its own. A company that owns its plants and inventory carries different economics than one that outsources production. Keep balance sheets roughly comparable, or at minimum know precisely where they differ so you can adjust.

Where to Find Candidate Peers

A stock screener is the obvious starting point, but the filings themselves are a better source, and they're all free on EDGAR.

• The 10-K, Item 1 and Item 1A. Companies describe their competitive landscape in the business section and often name specific competitors in the risk factors. Management's own view of who they lose deals to is a strong comparability signal.
• The proxy statement (DEF 14A). The compensation committee discloses the peer group it uses to benchmark executive pay, and boards put real thought into that list. Just watch for aspirational names padding it upward, since larger peers help justify larger pay packages.
• EDGAR full-text search. Search your target's name across other companies' filings. Any company that names your target as a competitor probably belongs on the candidate list.
• Earnings call transcripts. When analysts ask management about competition, the names that keep coming up are the real peer set, whatever the GICS code says.
• Recent M&A in the space. Acquisitions tell you which companies buyers treated as substitutes, and the deal multiples give you an extra valuation reference point.

Building the Peer Group Step by Step

Here's the sequence I run, in order.

1. Start wide. Pull every company in the same GICS sub-industry or SIC code, then add names surfaced from the filings sources above. Twenty to forty candidates is normal at this stage.
2. Filter by business model. Remove anything that makes money a different way. If the target is a SaaS company, hardware makers, consultancies, and systems integrators come off the list no matter how adjacent they look.
3. Filter by size. Drop companies dramatically larger or smaller, keeping the group within about three to five times the target's revenue.
4. Filter by growth. If the target grows around 15%, a peer growing 50% or shrinking 5% is answering a different valuation question.
5. Check margins. Compare gross and operating margins across the survivors and remove clear outliers, or note the reason they diverge before keeping them.
6. Sanity-check what's left. Skim a recent earnings release or investor presentation for each finalist. You're confirming the story matches the numbers, and you'll occasionally catch a pivot or a pending merger that disqualifies a name.

You should land on four to eight companies that genuinely resemble the target on the dimensions that drive valuation, which beats a list of twenty that share an industry code and nothing else. And if the honest answer is only two or three names, use them. A small clean peer group is worth more than a padded one, though in that case you'll want to lean harder on intrinsic methods like a DCF to triangulate.

A Worked Example with Made-Up Numbers

Say you're valuing a hypothetical vertical SaaS company that serves dental practices: $400 million in revenue, growing 25% a year, 78% gross margins, modest net debt. A screener returns fifteen application software names, and then the filters go to work.

• Three are horizontal enterprise suites with over $5 billion in revenue. Cut for size.
• Two are license-and-maintenance vendors mid-transition to subscriptions. Cut for business model, since the transition distorts both their reported growth and their margins.
• Two are growing under 5%. Cut for growth.
• One shows 45% gross margins because hardware dominates its revenue mix. Cut for margin profile.

That leaves roughly six vertical SaaS names of broadly similar size and growth. Now suppose they trade between 6x and 9x forward revenue with a median near 7x, and your target sits at 5x. That gap is interesting precisely because you've already eliminated the boring explanations. What remains is either something real the market is pricing (customer concentration, a slipping retention metric buried in the 10-Q, litigation) or an actual mispricing, and either answer is worth having. Every number in this example is invented, but the mechanics are exactly what you'd run with real ones.

Using the Peer Group Once You Have It

With the group set, compute the standard multiples for each peer: EV/Revenue, EV/EBITDA, P/E, and whatever metric the industry itself watches, whether that's EV per subscriber or revenue per location. Lean on the median rather than the mean, because one odd peer can drag an average a long way.

Then interrogate the gaps. If the target trades at a discount to the group, work out why before calling it cheap. Slower growth, weaker margins, a controlling shareholder, pending litigation, or messy accounting are all legitimate reasons for a discount. If none of them apply, you may have found something worth a much deeper look. The same logic runs in reverse for premiums. Pay attention to the full range as well. If peers trade at 8x to 14x EBITDA and the target sits at 6x, trading below the entire range is a stronger statement than trading below the median, and it demands a specific explanation rather than a shrug.

Mistakes I Keep Seeing

Aspirational peers. A $200 million company can't borrow the multiple of a $20 billion market leader just because they play in the same industry. The leader's multiple already reflects scale, durability, and market position the smaller company hasn't earned yet.

Adopting sell-side comp tables unexamined. Analyst reports include peer groups, but coverage lists shape them. An analyst will sometimes include a company because they cover it, or leave out a better comparable because they don't. Rebuild the group yourself and treat theirs as one input.

Ignoring geography. A European company with European tax rates, labor costs, and growth dynamics is a shaky comp for a US company even when they sell the same product. Either adjust for the differences explicitly or stay within the same region.

Letting the group go stale. Companies pivot, get acquired, and see growth decelerate. A peer group that was defensible two years ago may be indefensible today. Refresh it at least annually, and sooner if the target's own business shifts.

Backing into the answer. This one is the most dangerous because it's invisible to the person doing it. If you catch yourself adding a peer mainly because it lifts the median multiple toward the valuation you already believe, stop, set the thesis aside, and rebuild the group from the filters alone before recomputing anything.

Make It Part of the Process

Careful peer selection costs maybe thirty extra minutes per company, and it improves every number downstream, because each multiple, premium, or discount you calculate inherits the quality of the comp set behind it. So build the group before you look at a single multiple, write down why each peer made the cut, and put a note on the calendar to revisit the list in a year. If you do that consistently, the valuation work sitting on top of it gets a lot easier to trust.