How to Build a Comparable Company Analysis

A comparable company analysis — comps, in banker shorthand — is one of the first things sophisticated sell-side clients ask about. Done well, it anchors your valuation story in market reality and gives your pitchbook credibility before a buyer has read a single page of the CIM. Done poorly, it exposes your firm to difficult questions in the first management meeting.

This guide walks through how experienced M&A advisors build a defensible comps analysis from scratch — how to screen the universe, clean the data, select the right multiples, and present a coherent valuation range that holds up under buyer scrutiny.

Tools like Bookbuild automate the research, comp selection, and formatting pipeline — compressing a 2-week pitchbook build to hours. Request early access →

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What Is a Comparable Company Analysis?

A comparable company analysis (also called trading comps or public comps) values a private company by benchmarking it against publicly traded peers. The logic is straightforward: if similar businesses trade at 8–12x EBITDA, a target with comparable margins and growth should trade in that range — adjusted for size, liquidity discount, and deal-specific factors.

Comps are a relative valuation method, not an intrinsic one. Unlike a DCF, which derives value from projected cash flows, comps anchor value in what the market is currently paying for comparable businesses. In M&A, both methods appear in the same pitchbook — the comps set the market context, the DCF tests the fundamental case, and precedent transaction analysis shows what buyers have actually paid.

According to Goldman Sachs’ investment banking training materials, trading comps are considered the “market check” in any valuation — they reflect real-time investor sentiment rather than theoretical intrinsic value.

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Step 1: Screen the Initial Peer Universe

Start broad, then cut ruthlessly. Your initial screen should cast a wide net:

Primary filters:

• Same GICS or SIC classification (industry sector and sub-sector)
• Revenue range: typically 0.5x–3x the target company’s revenue
• Geography: same or comparable market (public US comps for a US target, ASX-listed for Australian targets)
• Public listing: NYSE, NASDAQ, LSE, ASX — wherever the most liquid peers trade

Secondary filters:

• Business model similarity (SaaS vs. professional services vs. manufacturing all trade on different frameworks)
• Revenue mix (recurring vs. project-based vs. transactional)
• Customer concentration (a company with 60% customer concentration is not truly comparable to one with distributed revenue)
• Growth profile (fast-growing comps carry premium multiples that distort the median)

A typical screening process returns 25–50 candidates before refinement. Use Capital IQ, Bloomberg, or FactSet to run the initial screen. Bookbuild users get instant access to a pre-screened database of 332K deal comps — the screening step is automated.

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Step 2: Refine the Set to 6–12 True Peers

The initial screen gives you candidates. This step is where judgment matters most.

For each candidate, ask:

• Do they compete in the same end market, or just the same broad sector?
• Would a sophisticated buyer of the target also look at this company as a comparable investment?
• Is the margin profile similar enough that the same EV/EBITDA multiple is meaningful?

Experienced bankers know that true comparability is about business model, not just SIC codes. A specialty distribution business is not truly comparable to a general logistics company even if they share an industry classification. Similarly, a company that recently completed a large acquisition may show distorted trailing EBITDA — flag it and consider using NTM estimates instead.

Remove obvious outliers:

• Companies with negative EBITDA (pre-profitability comps trade on revenue multiples, which should be presented separately)
• Companies with one-time charges that distort the trailing twelve months (LTM)
• Companies that have announced a pending acquisition (their stock price is already reflecting deal value, not standalone trading)

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Step 3: Collect and Standardize Financial Data

Once you have your final comp set, pull the following data points for each company:

Market data (as of your valuation date):

• Share price
• Diluted shares outstanding (fully diluted, including options and warrants)
• Market capitalization
• Net debt (total debt minus cash and equivalents)
• Enterprise Value = Market Cap + Net Debt

Income statement data (LTM and NTM estimates):

• Revenue (LTM actuals + NTM consensus estimates)
• Gross profit and gross margin
• EBITDA (LTM actuals + NTM consensus estimates)
• EBIT
• Net income

Key ratios:

• Revenue growth (1-year historical, projected)
• EBITDA margin
• Gross margin

Standardization matters. If one company capitalizes software development costs and another expenses them, their EBITDA figures are not apples-to-apples. Adjust to a consistent accounting basis before calculating multiples. PwC’s M&A guidance recommends always disclosing any adjustments made to reported EBITDA when presenting comps to buyers.

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Step 4: Calculate Trading Multiples

With enterprise values and financial metrics standardized, calculate the following multiples for each comp:

EV/EBITDA (most common in M&A):

• LTM EV/EBITDA
• NTM EV/EBITDA (using consensus analyst estimates)

EV/Revenue:

• Most relevant for high-growth companies with compressed margins
• LTM and NTM versions

EV/EBIT:

• Useful when depreciation policies vary significantly across the comp set

Price/Earnings (P/E):

• Less commonly used in M&A valuation; more relevant for capital markets comps

For each multiple, calculate:

• Minimum, 25th percentile, median, 75th percentile, maximum
• Mean (present alongside median; flag if they diverge significantly — signals outliers)

Most pitchbooks present comps in a tombstone-style table: companies listed by descending EV, with columns for each multiple. Keep the table clean. Bankers reading under time pressure will look at the median first.

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Step 5: Apply Multiples to the Target

With the range established, apply to your target’s metrics to derive an implied valuation range.

Implied Enterprise Value:

• Median EV/EBITDA × Target LTM EBITDA = Low case
• 75th percentile EV/EBITDA × Target NTM EBITDA = High case

Experienced advisors present a range, not a point estimate. A $80M–$120M implied valuation range is far more defensible than “$105M” — and sophisticated buyers know the imprecision inherent in any relative valuation.

Adjustments to the implied range:

1. Control premium: Public comps reflect minority trading values. In an M&A transaction, buyers pay a control premium — typically 20–35% above the unaffected share price for public targets. For private company comps used in a sell-side mandate, this may already be reflected in your precedent transaction analysis, so avoid double-counting.

2. Size discount: Small private companies trade at a discount to large public peers, reflecting lower liquidity and higher execution risk. Depending on the size gap, a 10–25% discount to the median may be warranted.

3. Company-specific adjustments: Customer concentration, margin variability, key-man risk, or pending litigation may warrant further discounts or warrant an explanatory footnote.

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Step 6: Present the Comps in the Pitchbook

The comps table belongs in the valuation section of your pitchbook or CIM. Presentation conventions:

• Table format: Companies in rows, multiples in columns. Sort by enterprise value (largest to smallest) or alphabetically if sizes are similar.
• Highlight the target: Add a shaded row showing where the target falls within the comp set — implied multiples at your proposed valuation, with the median highlighted.
• Football field chart: A horizontal bar chart showing the implied valuation range from each method — comps, precedent transactions, and DCF — is standard in sell-side pitchbooks. It communicates the full valuation story at a glance.
• Footnotes: Date of market data, any EBITDA adjustments, source (Capital IQ, Bloomberg, etc.)

According to Bain & Company’s M&A practitioner research, valuation credibility is often the deciding factor in whether a management team endorses a banker’s process. A well-constructed comps analysis — clearly sourced, properly adjusted, and presented without overclaiming precision — builds that credibility from the first meeting.

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Common Mistakes to Avoid

Using too broad a comp set. Including every company in the sector to create the appearance of more data dilutes the analysis. A focused set of 8 true peers is more credible than 20 loosely related companies.

Ignoring the composition of EBITDA. Adjusted EBITDA figures from management should be scrutinized before applying multiples. If management’s adjustments aren’t defensible to buyers, build the comps on reported figures and address adjustments separately.

Presenting comps without context. The table is not self-explanatory. Walk the client through what the median means, why you excluded certain outliers, and what the range implies for their business specifically.

Anchoring too early. In a sell-side M&A process, premature anchoring on a specific valuation — before testing buyer appetite — can limit outcomes. Use comps to frame expectations, not to set a hard floor before receiving IOIs.

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How Bookbuild Automates the Comps Build

For boutique advisors running lean teams, the comps analysis is often the most time-consuming slide in the pitchbook. Sourcing data, standardizing financials, and formatting the table correctly can take a full day of analyst work.

Bookbuild’s platform pulls from a database of 332K deal comps, automatically calculates the standard multiple set, and surfaces the formatted comps table into the live pitchbook. The advisor reviews, adjusts, and applies their judgment — the platform handles the data plumbing.

The result is a client-ready comps analysis in hours, not days. Request early access →

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Related Resources

• What Is a Comparable Company Analysis?
• What Is Precedent Transaction Analysis?
• How to Write an Investment Banking Pitchbook
• What Is EBITDA?
• What Is Adjusted EBITDA?
• What Goes in a CIM Executive Summary?