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Glossary

Comparable Company Analysis

A relative valuation methodology that estimates a company's value by comparing its financial metrics and trading multiples to those of similar publicly listed companies in the same industry.

What Is Comparable Company Analysis?

Comparable company analysis — commonly called “trading comps” — is one of the three core valuation methodologies used in M&A, alongside discounted cash flow analysis and precedent transactions (Investopedia). The approach values a business by identifying publicly traded companies with similar characteristics and applying their valuation multiples to the target’s financial metrics.

The logic is straightforward: if similar companies trade at a given multiple of earnings or revenue, the target should be worth a comparable multiple of its own earnings or revenue, adjusted for differences in growth, margins, scale, and risk.

How Comps Work

Step 1: Select the Peer Group

The analyst identifies 8–15 publicly listed companies that share key attributes with the target:

  • Industry and sub-sector — companies operating in the same market
  • Size — measured by revenue, enterprise value, or market capitalisation
  • Geography — companies in the same region or with similar market exposure
  • Growth profile — similar revenue growth rates and margin trajectories
  • Business model — comparable customer bases, pricing models, and competitive positioning

Step 2: Gather Financial Data

For each peer, the analyst collects standardised financial data, typically from equity research, filings, or financial data platforms. Key metrics include revenue, EBITDA, net income, and enterprise value.

Step 3: Calculate Multiples

Common multiples include:

  • EV / Revenue — useful for high-growth or pre-profit companies
  • EV / EBITDA — the most widely used M&A multiple, as it strips out capital structure and tax differences
  • P / E (Price to Earnings) — common in public market analysis but less used in M&A due to leverage and tax distortions
  • EV / EBIT — useful when depreciation policies differ significantly across peers

Step 4: Apply to the Target

The analyst applies the median or mean peer multiple to the target’s corresponding metric to derive an implied valuation range. For a deeper look at how this fits alongside other approaches, see our guide to M&A valuation.

Strengths and Limitations

Strengths:

  • Market-based and observable — grounded in real trading data rather than assumptions
  • Quick to construct relative to a full DCF model
  • Provides a useful sanity check and benchmark for other valuation methods

Limitations:

  • Assumes the market is pricing peers correctly — if the sector is overvalued or undervalued, the comps will be too
  • Finding truly comparable companies is difficult, especially for niche or diversified businesses
  • Does not capture control premium — public trading multiples reflect minority stakes, not acquisition-level pricing
  • Differences in accounting standards, fiscal years, and reporting currencies add noise

Comps vs DCF vs Precedent Transactions

In sell-side advisory, bankers typically present all three methodologies in a valuation “football field” chart (Corporate Finance Institute). Trading comps show where the market values similar businesses today. DCF shows intrinsic value based on projected cash flows. Precedent transactions show what acquirers have historically paid, including control premiums. The interplay between these three methods is central to how advisors value a business for sale.

Comparable Company Analysis in Asia Pacific

Running comps across Asia Pacific markets introduces complexity not found in single-market analyses. Peer groups often span multiple jurisdictions with different accounting standards (IFRS, local GAAP, J-GAAP), reporting currencies, and fiscal year-ends. Sectoral definitions vary — a logistics company in Australia may have a very different business mix from one in Vietnam. Liquidity differences matter too; thinly traded stocks in emerging ASEAN markets may produce unreliable multiples. AI-native platforms like Amafi help analysts source and normalise financial data across APAC markets, enabling more robust peer benchmarking for cross-border transactions.

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