How to Build a Trading Comps (Comparable Companies) Analysis

What a trading comps analysis is

A trading comparable companies analysis (comps, for short) values a business by reference to how the public market is currently pricing similar businesses. Where a DCF values a company on its own projected cash flows, comps value it relative to peers, converting each peer's market price into a multiple of some financial metric, then applying that multiple to the subject company. It is a relative valuation method: it tells you what the market is paying for similar businesses right now, not what a business is intrinsically worth on its own merits.

Interviewers ask about comps because building a clean set requires real judgment: which companies actually belong in the peer group, which multiple fits the situation, and how to handle a peer set that isn't perfectly comparable, which it never is.

Selecting the peer set

A good comp set matches the subject company as closely as possible on the factors that actually drive valuation: industry and end markets, business model, size (revenue or market cap), growth rate, margin profile, and geography. Screening usually starts broad (same industry classification) and narrows from there, dropping companies that are too different in size, growth, or profitability to trade at a genuinely comparable multiple.

The multiples: EV/EBITDA, EV/Revenue, and P/E

The multiple has to pair a numerator and denominator that reflect the same claim on the business, exactly the logic covered on the enterprise value guide. EV/EBITDA and EV/Revenue are capital-structure-neutral: since both EV and EBITDA (or revenue) sit before financing effects, these multiples let you compare companies with different amounts of debt on equal footing, which is why EV/EBITDA is the most common multiple in comps work. P/E, price divided by earnings per share, pairs equity value with net income, both already affected by capital structure, making it more useful when comparing companies with genuinely similar leverage, or in sectors like financials where an enterprise value framework doesn't apply cleanly.

Multiples are usually shown on both an LTM (last twelve months, actual reported results) and NTM (next twelve months, analyst estimates) basis. NTM multiples strip out the effect of a peer having just had an unusually strong or weak trailing year, and are generally considered more forward-looking and comparable, but rely on the accuracy of analyst forecasts.

Building the table and applying it to the subject

1. For each peer, calculate enterprise value and equity value from public data (share price, shares outstanding, debt, cash).
2. Divide by each peer's EBITDA, revenue, and EPS to get its multiples.
3. Take the median (not the mean) across the peer set for each multiple, since the median is far less distorted by one or two outlier companies than a simple average.
4. Apply the peer median multiple to the subject company's own metric to get an implied enterprise value (or equity value, for P/E), then bridge to a per-share price the same way the DCF guide does.

Worked example: three peers trade at 7.5x, 8.0x, and 9.0x EV/EBITDA (median 8.0x). The subject company has $150mm of EBITDA.

Limits of the method

No two companies are perfectly comparable, so a comps-based value is always an approximation shaped by whichever peers were chosen. The whole peer set can also be mispriced together, during a sector-wide bubble or downturn the comps simply repeat that mispricing rather than correcting for it, which is exactly why comps are typically triangulated against a DCF and, in an M&A context, precedent transactions (past deals for similar companies, which unlike trading comps include the control premium a buyer pays to acquire the whole company).

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