Choosing “comparable companies” is one of the oldest exercises in finance interviews.
Most candidates treat it as a data task: open CapIQ, filter by industry, copy the tickers.
Interviewers see it very differently.
For them, a comp discussion is an x-ray of how you think:
Do you understand the business model?
Can you separate signal from noise?
And, crucially, can you defend your choices when someone senior pushes back?
This article shows you how to move beyond list-pulling and build comp sets that actually make sense—so you can argue like an analyst, not just behave like a database user.
I. What Interviewers Are Really Testing When They Ask for Comps
On the surface, comps are about valuation:
- EV/EBITDA, EV/Revenue, P/E, etc.
But in an interview, comp selection is primarily a test of:
- Business model understanding
Can you articulate how the target makes money and who else really plays the same game? - Structured thinking
Do you use explicit criteria, or do you just “grab names”? - Skepticism toward raw data
Do you accept whatever the screen shows, or do you question outliers, segments, and one-off events? - Communication under pressure
Can you explain your choices in two or three tight sentences when someone interrupts you?
If your comp set is sloppy, every follow-up question becomes dangerous.
If your comp set is well reasoned, the discussion becomes your biggest opportunity to stand out.
II. Defining “True Peers”: A Structured Framework
Instead of starting with “Who looks similar in CapIQ?”, start with criteria.
Think in four dimensions.
1. Business Model (Non-Negotiable)
Ask first: Do they make money in the same way?
Examples:
- SaaS vs. perpetual license
- Asset-light broker vs. balance sheet lender
- Contract manufacturer vs. branded OEM
If the revenue engine, cost structure, and value proposition are fundamentally different, the company is not a peer—no matter what the industry code says.
2. Size and Life Cycle
You want companies that live in the same growth and profitability regime:
- Revenue in a comparable range (not necessarily identical, but the same order of magnitude)
- Similar revenue growth (e.g. 10–20% vs. 0–3%)
- Similar margin shape (e.g. low-margin volume business vs. high-margin software)
Putting a €300m revenue, 20% growth SaaS company in the same comp set as a €5bn, 5% growth incumbent is a red flag. The market prices these risk profiles differently.
3. Geography and Regulatory Environment
Geography isn’t a cosmetic detail; it shapes economics:
- labor cost and employment rules
- data privacy / regulation (e.g. GDPR, HIPAA, sector-specific regimes)
- currency risk and inflation
- tax & capital requirements
You can still mix geographies, but you must acknowledge the differences:
“I included one US peer as a benchmark for scaled economics, while the core of the set remains European to reflect the same regulatory and customer environment.”
That’s the difference between copy-paste and judgment.
4. Capital Structure and Listing Status
Two often-neglected questions:
- Is this a highly leveraged business that depresses equity multiples?
- Is this company even publicly traded, or is the data patchy / illiquid?
For interview purposes, you don’t need a full-blown capital structure analysis, but you should be aware that:
- a heavily levered peer may look “cheap” on P/E but normal on EV/EBITDA
- thinly traded small caps may generate noisy, less reliable market multiples
III. Detecting Data Noise Before It Destroys Your Multiples
Once you have a logical starting set, the next job is to clean it.
1. One-offs and Distortions
Ask what happened in the last 12–24 months:
- large restructurings or impairments
- spin-offs, major acquisitions, discontinued operations
- accounting changes (e.g. revenue recognition, leases)
Any of these can distort EBITDA or net income and, by extension, your multiples.
In an interview, you don’t need a full forensic analysis, but you should say something like:
“I would check whether last year’s results include unusual restructuring charges or disposals that distort EBITDA. If so, I would either adjust the numbers or drop the company from the set.”
That sentence alone signals you understand why raw data can’t be taken at face value.
2. Segment Mismatch
Many “peers” are diversified:
- A “software” company with 40% low-margin services
- An “industrial” with a high-margin spare parts business
- A “retailer” with a significant finance/credit arm
If your target is a pure-play, you shouldn’t compare it to conglomerates without at least acknowledging the difference.
You can either:
- exclude the diversified player, or
- use it as a secondary reference, clearly flagged as such.
3. Outliers and Robust Statistics
Even with a clean set, some multiples will cluster, and one or two will be far away.
- Don’t just calculate the mean and call it a day.
- Use medians and interquartile ranges as your mental default.
In an interview, interviewers don’t expect you to run stats in Excel, but they love hearing:
“I’d focus on the median EV/EBITDA and treat very high or low values as outliers unless I can explain them structurally.”
That line is enough to mark you as someone who understands how to use statistics in practice.
IV. A Step-by-Step Comp Selection Process You Can Reproduce in Interviews
You can turn all of this into a simple, repeatable algorithm you describe out loud.
Step 1 – Describe the target clearly
Industry, revenue scale, growth, margin profile, business model, geography.
Step 2 – Define screening criteria
For example:
- SaaS / recurring revenue > 80%
- Revenue €100–500m
- Revenue growth 10–25%
- EBITDA margin 15–30%
- Primarily European or US-listed
Step 3 – Pull an initial list
From a database or from the interviewer’s hints. Don’t worry about being perfect immediately.
Step 4 – Clean the set
Drop companies that clearly fail the business model or stage test. Flag companies with mixed segments or recent major events.
Step 5 – Sanity-check the results
Ask yourself:
- Do the names feel like they compete for the same customers?
- Are the growth and margins in a coherent band?
- Are any multiples obviously “too good to be true”?
Then say your logic out loud. That’s where the value is created.
V. Interview Case Study: Building a Defensible SaaS Comp Set
Scenario
You are given a European cloud-based HR software vendor:
- Revenue: ~€300m
- Growth: ~20% per year
- EBITDA margin: ~22%
- Business model: subscription SaaS
- Geography: primarily Europe, some US customers
How a weak candidate responds:
“I’d use Workday, SAP, and Oracle as comps—they’re big in HR software.”
Everything about this is wrong: size, stage, and business model mix.
How a strong candidate responds:
“I’d first define the peer criteria: subscription-based HR or HCM SaaS, revenue in roughly the €100–800m range, double-digit growth, EBITDA margin in the 15–30% range, and primarily Europe or US so that the regulatory and customer environments are comparable.
Using that filter, I might start with [Company A] and [Company B] in Europe and [Company C] in the US as scaled peers. I would treat a larger name like Workday as a secondary benchmark for what margins look like at global scale but wouldn’t let it drive the median multiple, given the size and product breadth gap.”
Notice what you’re doing:
- You lead with criteria, not company names
- You acknowledge why you include or demote each peer
- You show you’re aware of scale and scope effects
That’s exactly the reasoning interviewers want to hear.
VI. How to Communicate Your Reasoning Under Pressure
In a real interview, you don’t have time for long speeches. You need tight, reusable sentences.
Here are examples you can plug into almost any comp discussion:
- On business model: “I focused on companies with the same revenue model and margin structure—pure subscription SaaS—because mixed license or hardware businesses would distort the multiples.”
- On size and growth: “I kept the comp set to mid-market players with similar revenue and 10–20% growth so we’re not comparing a niche player to a global incumbent.”
- On geography: “Most peers are European to reflect the same regulatory and customer environment; I included one US name as a benchmark for scaled economics.”
- On outliers: “This company screens cheap on P/E, but that’s mainly due to higher leverage. On an EV/EBITDA basis it’s in line with the rest, so I wouldn’t let it pull down the median.”
Being able to deliver lines like these calmly and quickly is what makes you sound like someone who has actually done this on the job.
VII. Common Traps (and How to Avoid Them)
- The “industry code” trap
Blindly trusting SIC/NAICS classifications. Always sanity-check whether the business models really match. - The “all the names I know” trap
Filling the list with famous brands. If you can’t explain why they’re peers in two sentences, they probably aren’t. - The “database says so” trap
Relying on a pre-built screener from a data provider without reviewing segments, leverage, or one-offs. - The “everything is a peer” trap
Keeping questionable names “because more data is better”. In valuation, a smaller, cleaner set beats a larger, messy one.
VIII. Conclusion: Comps as a Window into Your Thinking
Selecting comps is not a clerical task. It’s a compact test of:
- how you understand business models
- how you handle imperfect data
- how you structure and defend your reasoning
Anyone can press “export to Excel”.
Very few candidates can look at a noisy list of “similar companies”, impose structure on it, and explain in crisp language why these peers are the right ones for the job.
If you:
- Start from the target’s economics,
- Define clear peer criteria,
- Clean the data with basic skepticism, and
- Practice saying your logic out loud,
you’ll turn every comp discussion from a risk into an advantage—and that is exactly what interviewers are looking for.
That’s what turns your comp discussion into a competitive edge.
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