If you want to work in investment banking, you need to understand comparable company analysis — often called “comps” — inside and out. It is one of the three core valuation methodologies used on virtually every deal, and it is almost guaranteed to come up in your technical interviews. Whether you are a sophomore building your skills from scratch or a junior preparing for superday, this guide walks you through how comps work, how to build them, and how to talk about them confidently in front of a banker. Make sure to grab our Technical Cheatsheet as a companion resource while you read.
What Is Comparable Company Analysis?
Comparable company analysis is a relative valuation methodology that estimates the value of a company by looking at how similar publicly traded companies are priced in the market. The core idea is simple: if Company A and Company B operate in the same industry with similar growth profiles and margins, the market should value them at roughly similar multiples of their earnings, revenue, or cash flow.
Comps are sometimes called “trading comps” to distinguish them from “transaction comps” (which are used in precedent transactions analysis — a separate methodology covered in detail on the WSMM blog). Trading comps reflect what the market is willing to pay for a business on a going-concern, minority-interest basis — in other words, what a share of stock is worth today.
Bankers use comps to:
- Establish a valuation range for an IPO or M&A transaction
- Benchmark a company’s performance against its peers
- Build the first pillar of a “football field” valuation summary
- Check the reasonableness of a DCF or LBO output
Step 1 — Select the Right Peer Group
The most important — and most subjective — step in building a comps analysis is choosing which companies to include. A poorly selected peer group will produce meaningless multiples, no matter how perfect your spreadsheet is.
Criteria for Selecting Peers
When selecting comparable companies, you should consider the following dimensions:
- Industry and business model: This is the baseline. A SaaS company should be compared to other SaaS companies, not industrial manufacturers. Go narrow first, then expand if you do not have enough peers.
- Revenue scale: A $50M revenue company and a $5B revenue company will trade at very different multiples due to liquidity, analyst coverage, and market perception. Try to stay within roughly one order of magnitude.
- Growth profile: High-growth companies command premium multiples. Mixing a 40% revenue grower with a 5% grower will distort your analysis unless you explicitly note it.
- Geography: US-listed companies generally trade at different multiples than European or Asian peers due to investor base differences and accounting standards.
- Profitability: Pre-profit companies are often valued on revenue multiples, while mature businesses are valued on EBITDA or earnings multiples. Do not mix them without a clear rationale.
A typical comps table has 8 to 15 companies. Fewer than 6 and the analysis lacks credibility; more than 20 and you likely have too many tangential peers diluting your signal.
Step 2 — Gather and Spread the Financial Data
Once you have your peer group, you need to pull financial data for each company. In a real banking context, analysts use Capital IQ, FactSet, or Bloomberg. For practice, you can use SEC filings and company investor relations pages.
Key Financial Metrics to Spread
For each company, you typically want to collect:
- Market capitalization: Current share price multiplied by diluted shares outstanding (include options and convertible instruments using the treasury stock method).
- Enterprise value (EV): Market cap plus total debt, plus preferred equity, plus minority interest, minus cash and cash equivalents. EV represents the total value of the business to all capital providers.
- Revenue: Last twelve months (LTM) actual revenue, plus estimates for the next one to two fiscal years (NTM).
- EBITDA: Earnings before interest, taxes, depreciation, and amortization — the most commonly used profitability metric for EV-based multiples.
- EBIT: Useful for capital-intensive businesses where D&A is significant.
- Net income and EPS: Required to calculate the price-to-earnings (P/E) ratio.
- Free cash flow: Relevant for some industries, particularly capital-light businesses.
Calendarize all financials to the same fiscal year end before comparing. A company with a June fiscal year-end needs to be restated to a December equivalent so your LTM figures are apples-to-apples. Our Technical Cheatsheet includes a quick reference for the most common calendarization formulas.
Step 3 — Calculate the Valuation Multiples
With your data spread, you can now calculate valuation multiples. These are ratios that express the company’s value relative to a financial metric.
Enterprise Value Multiples
EV multiples are capital-structure neutral — they compare total business value (debt + equity) to operating metrics that accrue to all capital providers:
- EV/Revenue: Common for high-growth or pre-profit companies. Typical ranges: 1x–3x for mature businesses, 5x–15x+ for high-growth SaaS.
- EV/EBITDA: The most widely used multiple across industries. Strips out capital structure and tax differences. Typical ranges vary by industry — software might trade at 15x–25x, while industrials might trade at 6x–10x.
- EV/EBIT: Accounts for depreciation, which matters in capital-intensive industries like manufacturing or telecommunications.
Equity Multiples
Equity multiples are capital-structure dependent and only meaningful for companies with similar leverage profiles:
- Price/Earnings (P/E): Share price divided by EPS. Widely used but affected by leverage, tax rates, and non-cash charges.
- Price/Book (P/B): Most relevant for financial institutions like banks and insurance companies.
For each multiple, calculate the mean, median, 25th percentile, and 75th percentile across your peer set. Analysts typically focus on the median and exclude outliers that would skew the range.
Step 4 — Apply Multiples to the Target Company and Derive an Implied Value
Now you apply the peer group’s multiples to the target company’s financial metrics to derive an implied valuation range.
For example: if your peer group trades at a median EV/EBITDA of 12.0x, and your target company has LTM EBITDA of $100M, the implied enterprise value is $1.2B. You then subtract net debt (debt minus cash) to arrive at implied equity value, and divide by diluted shares to get implied share price.
You should run this exercise across multiple metrics (LTM EBITDA, NTM EBITDA, NTM Revenue) and multiple multiples. The result is a range of implied values, not a single point estimate. Present the 25th to 75th percentile range as your primary output and explain the outliers.
This is where judgment matters. If the target company has faster growth than the median peer, you might argue it deserves to trade at the high end of the range. If it has lower margins, it might deserve a discount. This qualitative overlay is what separates a junior analyst who just runs the numbers from a senior banker who can actually defend the analysis in a pitch meeting.
Step 5 — Present the Analysis Clearly
In a real banking context, comps are presented as a formatted table — typically in Excel or PowerPoint — that shows each company’s key stats, multiples, and summary statistics. Interviewers will sometimes ask you to walk them through a comps table during a case study or modeling test.
When presenting, be ready to discuss:
- Why you chose the peer group you did (and who you excluded and why)
- Which multiple is most relevant for this industry and why
- How the target compares to peers on growth and margin metrics
- What the implied valuation range is and whether it seems reasonable
- How the comps output compares to your DCF or precedent transactions analysis
Practicing this out loud is essential. The WSMM coaching approach emphasizes verbal delivery as much as technical accuracy — because in an interview, how you explain it is just as important as getting the numbers right. You can also check out our student interviews to see how past students handled technical questions in real superdays.
Common Mistakes Candidates Make With Comps
After coaching hundreds of students through technical interviews at top banks, here are the most frequent errors I see:
- Using market cap instead of enterprise value for EV multiples: EV/EBITDA uses EV in the numerator, not market cap. Mixing these up is an immediate red flag.
- Ignoring calendarization: Comparing a December fiscal year company to a June fiscal year company without adjusting will produce nonsense LTM figures.
- Not scrubbing for one-time items: EBITDA should be adjusted (“scrubbed”) for non-recurring items like restructuring charges, litigation settlements, or COVID-related costs. Banks call this “adjusted EBITDA” or “normalized EBITDA.”
- Selecting too-narrow or too-broad a peer group: Including a competitor that just received a takeover bid will inflate its trading multiple artificially. Excluding the most relevant peers because they make you look bad is equally problematic.
- Treating the output as a precise number: Comps produce a range, not a number. Never present a single implied value — always show the high and low and explain the range.
If you want a structured way to practice these concepts, our Free Course covers valuation fundamentals in detail, and our Free Resources page has additional practice materials.
How Comps Fit Into a Full Valuation
Comparable company analysis is almost never used in isolation. In a real pitch book or fairness opinion, bankers present multiple valuation methodologies side by side in a “football field” chart. The three primary methodologies are:
- Comparable company analysis (trading comps) — minority interest, going-concern value
- Precedent transactions analysis — includes a control premium, reflects M&A market conditions
- Discounted cash flow (DCF) — intrinsic value based on projected free cash flows
In an M&A context, comps typically represent the floor of a valuation range because they do not include a control premium. Precedent transactions usually produce higher multiples because acquirers pay a premium to gain control. The DCF can go either way depending on your assumptions.
Understanding how these three methodologies interact — and being able to explain why they might diverge — is what separates candidates who get offers from candidates who do not. Check out our WSMM Track Record to see where our students have landed after mastering these concepts.
Want Personalized Interview Coaching?
Understanding comparable company analysis conceptually is one thing — being able to explain it clearly under pressure in a Goldman or Morgan Stanley interview is another. At Wall Street Mastermind, we work with students one-on-one to make sure you can walk through comps, DCFs, and merger models fluently and confidently.
If you are serious about breaking into investment banking, apply to work with us here. We have helped hundreds of students land offers at bulge bracket and elite boutique banks, and we can help you do the same. You can read what past students have said on our Trustpilot page and watch their stories on our student interviews page.
