Comparable Company Analysis: How to Run Trading Comps

Comparable company analysis -- commonly known as “trading comps” -- is one of the three pillars of M&A valuation, alongside discounted cash flow analysis and precedent transactions. Trading comps derive a target’s value by examining how the public markets price similar businesses. The logic is simple: if five comparable public companies trade at 10-12x EBITDA, a similar private company should be worth roughly the same range, adjusted for size, growth, and liquidity differences. This guide walks through the full trading comps workflow, from peer selection through implied valuation, with the practical nuances that separate rigorous analysis from superficial benchmarking.

What Are Trading Comps?

Trading comps is a relative valuation method that benchmarks a target company against a set of publicly traded peers. By calculating the enterprise value multiples (and sometimes equity multiples) at which peer companies trade, the analyst derives an implied valuation range for the target. The approach assumes that markets are reasonably efficient and that similar businesses should trade at similar multiples, adjusted for differences in growth, profitability, risk, and capital structure.

Unlike a DCF analysis, which requires detailed multi-year projections, trading comps rely on observable market data and can be updated in real time as stock prices move. This makes comps the fastest valuation method and the one most responsive to current market conditions. However, this market sensitivity is also its greatest weakness: if the market is overvaluing or undervaluing a sector, your comps-based valuation will inherit that bias.

The Trading Comps Workflow

Selecting Peer Companies

Peer selection is the single most important and subjective step in the comps process. A poorly selected peer group will produce a misleading valuation regardless of how carefully you calculate the multiples. The goal is to identify 5-15 companies that share the same fundamental economic characteristics as the target.

Start with a broad universe using industry classification codes (SIC, NAICS, GICS), then narrow progressively using the criteria above. Financial databases like S&P Capital IQ, FactSet, PitchBook, and Bloomberg provide screening tools that automate much of this process. Always review the resulting list qualitatively -- no screening algorithm captures every nuance of business model comparability.

When the peer group is small (fewer than five companies), consider widening the industry definition or including adjacent sectors. When it is large (more than 15), tighten your size and growth filters. Some practitioners create a “primary” peer group (closest matches) and a “secondary” peer group (directionally comparable) to provide context.

Key Valuation Multiples

The choice of multiple depends on the industry and the target’s financial profile. For a comprehensive breakdown of multiples by sector, see our valuation multiples guide.

EV/EBITDA

The dominant M&A multiple. Capital-structure neutral, eliminates non-cash charges, and is directly comparable across companies with different tax jurisdictions. EV/EBITDA is preferred for profitable, capital-efficient businesses. Always use adjusted EBITDA that normalizes for one-time items, stock-based compensation (depending on convention), and non-recurring expenses.

EV/Revenue

Used when EBITDA is negative, volatile, or less meaningful. Standard for high-growth technology companies, pre-profit biotech, and businesses in investment mode. EV/Revenue is also useful when comparing companies with different margin profiles, since it separates valuation from profitability. The relationship between EV/Revenue and margin is captured by: EV/Revenue = EV/EBITDA × EBITDA Margin.

P/E (Price-to-Earnings)

An equity-level multiple used primarily for financial institutions, mature consumer businesses, and public-market benchmarking. P/E is affected by capital structure (leverage increases P/E volatility), making it less useful for cross-company comparison in leveraged industries. Forward P/E (based on next-year consensus earnings) is generally more informative than trailing P/E.

EV/EBIT

Preferred over EV/EBITDA for capital-intensive businesses where depreciation is a real economic cost (manufacturing, infrastructure, airlines). EV/EBIT captures the impact of differing capital intensity across peer companies.

Normalizing Financials

Raw reported financials are rarely comparable across companies. Normalization adjusts for differences in accounting policies, one-time items, and fiscal year timing to create an apples-to-apples comparison.

• Calendarization: Align fiscal years to a common period. If your target has a December fiscal year-end but a peer has a June year-end, calendarize the peer’s financials using quarterly data.
• One-time items: Remove restructuring charges, litigation settlements, asset impairments, and gains/losses on disposals. These distort the underlying earnings power.
• Stock-based compensation: Controversial. Some analysts add back SBC (treating it as non-cash), while others do not (arguing it is a real economic cost that dilutes shareholders). Be consistent across the peer group and disclose your treatment.
• Lease accounting: Under IFRS 16 / ASC 842, operating leases are capitalized. Ensure all peers are treated consistently, particularly for retailers, airlines, and other lease-heavy businesses.
• M&A adjustments: If a peer completed a material acquisition mid-year, use pro-forma financials that include the acquired company for the full period.

Calculating the Implied Valuation Range

Once you have calculated the relevant multiples for each peer, derive the statistical measures: mean, median, 25th percentile (Q1), and 75th percentile (Q3). The interquartile range (Q1 to Q3) typically provides the most defensible valuation range, as it excludes outliers.

Apply the peer multiples to the target’s normalized financial metrics:

Implied EV = Target Adjusted EBITDA × Peer EV/EBITDA Multiple

For a target with $25M adjusted EBITDA and a peer median EV/EBITDA of 10.5x (range 9.0x to 12.0x), the implied enterprise value range is $225M to $300M, with a midpoint of $262.5M.

Applying Premiums and Discounts

Trading comps produce a minority, marketable value -- what a small, liquid stake in a publicly traded company is worth. To translate this into an acquisition price for a private company, several adjustments are necessary:

• Control premium (+15% to +40%): Acquiring 100% of a company conveys control benefits (board seats, strategy changes, synergy realization). Historical control premiums average 25-35% across sectors. Apply this premium if the target is private or if you are evaluating a public takeover.
• Size discount (-10% to -30%): Smaller companies are riskier, less diversified, and less liquid. The size discount compensates for these additional risks. See the GF Data size premium tables for empirical data.
• Illiquidity discount (-15% to -30%): Private company shares cannot be sold on an exchange. The lack of marketability reduces value relative to publicly traded peers. The combined size and illiquidity discount for a small private company can be 20-40%.
• Growth premium or discount: If the target is growing materially faster or slower than the peer median, adjust the implied multiple accordingly. A regression of EV/EBITDA against revenue growth rates for the peer group can quantify this relationship.

The Football Field Chart

The football field chart is the standard format for presenting M&A valuation ranges from multiple methodologies side by side. Each bar represents the low-to-high range from a different approach, and the overlapping zone is typically the negotiation range.

The chart above illustrates a simplified football field. In practice, each bar would show the full range (low, midpoint, high) using a horizontal stacked bar or a range chart. The convergence zone -- where multiple methodologies overlap -- provides the strongest basis for negotiation and board presentation.

Comps vs. DCF vs. Precedent Transactions

Each valuation methodology answers a different question. Trading comps answer: “What would the market pay for this business today?” A DCF answers: “What is this business intrinsically worth based on its future cash flows?” Precedent transactions answer: “What have acquirers actually paid for similar businesses?” A robust valuation triangulates all three.

Trading comps tend to produce the lowest valuation range (minority value without control premium), DCF valuations cluster in the middle (depending on assumptions), and precedent transactions tend to produce the highest range (because they include control premiums and synergy expectations). When the three methods produce widely divergent ranges, it signals that the market is pricing the sector differently from fundamental value, or that the comparable sets need refinement.

Common Pitfalls

• Cherry-picking peers: Selecting only the highest-multiple peers to justify a higher price (or lowest to justify a lower price) destroys credibility. The peer group should be defensible to a skeptical counterparty.
• Ignoring growth differences: A peer group with 5-30% revenue growth dispersion will have massive multiple dispersion. Presenting the median without adjusting for growth is misleading.
• Stale data: Multiples change daily. Running comps with data from three months ago can produce materially different results, particularly in volatile markets.
• Mixing reported and adjusted EBITDA: If some peers use reported EBITDA and others use adjusted EBITDA, the resulting multiples are not comparable. Be consistent.
• Forgetting the equity bridge: Comps produce enterprise value. To get equity value (which determines the price per share or the check the buyer writes), subtract net debt, minority interest, and preferred stock.
• Over-reliance on a single multiple: EV/EBITDA may not tell the whole story. Cross-check with EV/Revenue, P/E, and industry-specific metrics (EV/subscriber, EV/bed, price/AUM) to build a more complete picture.

When Comps Mislead

Trading comps are a mirror of market sentiment, which means they inherit all of the market’s biases. During the 2021 technology bubble, SaaS companies traded at 20-40x revenue -- comps-based valuations during that period were wildly inflated relative to intrinsic value. By 2023, those same companies had compressed to 5-10x revenue. A comps analysis at either extreme would have produced misleading results.

Comps also mislead when the peer group is fundamentally different from the target. A regional niche software company with 10% growth is not comparable to a hyper-growth cloud platform, even if both are classified as “enterprise software.” The analyst must look beyond industry codes and assess true economic comparability.

Finally, comps do not capture company-specific value drivers. If a target has a unique patent portfolio, irreplaceable customer relationships, or a management team with a track record of exceptional execution, those factors will not be reflected in peer multiples. A DCF or strategic premium analysis is needed to capture idiosyncratic value.

Trading Comps Checklist

Conclusion

Comparable company analysis is the fastest and most market-grounded valuation method in the M&A toolkit. When done rigorously -- with careful peer selection, consistent normalization, and appropriate premiums and discounts -- it provides a powerful reality check against intrinsic valuation methods. The key is to treat comps as one input in a multi-method framework, not as the definitive answer.

Combine your trading comps with a thorough DCF analysis and a review of precedent transactions to build a valuation range that is defensible to boards, investors, regulators, and counterparties. The analyst who can explain why the target deserves a premium or discount to the peer median -- backed by data on growth, margins, and risk -- will produce the most credible and persuasive valuation.