Comparable Companies and the Application of Multiples

Comparable Companies and the Application of Multiples

Comparable Companies Analysis (Comps) — comparable companies analysis is the evaluation of a target company by comparing it with publicly traded peers. Selecting appropriate comparables and adjusting for differences are critical skills for accurate relative valuation.

Selecting Comparables

• Industry: same industry or sub-sector. A consumer staples company should not be compared to tech.
• Business model: similar operations. A retailer vs e-commerce, even if both are “retail”, may differ significantly.
• Size: similar market cap, revenue. Large caps trade differently than small caps.
• Geography: same or similar markets. An emerging market company vs developed market — different risk profiles.
• Growth profile: growth companies vs mature. High-growth deserves a higher multiple.
• Profitability: similar margins. A low-margin company shouldn't trade at the same EV/Sales as a high-margin one.

Finding Comparables

• Industry classification: GICS (Global Industry Classification Standard), SIC codes. Starting point for peer identification.
• Company filings: management often identifies competitors in 10-K. Useful starting point.
• Equity research: analysts cover peer groups. Research reports list comparables.
• Judgment: no perfect match. Use judgment to select the most similar companies.

Calculating Peer Multiples For each comparable: calculate the chosen multiples (P/E, EV/EBITDA, etc.) using the current price and relevant financials.

• LTM (Last Twelve Months): trailing financials. Actual, reported data.
• NTM (Next Twelve Months): forward estimates. Consensus analyst forecasts.
• Calendarization: align fiscal years if comparables have different fiscal year ends.

Analyzing Peer Multiples

• Range: min, max, median, mean of peer multiples. Shows spread in valuations.
• Median preferred: less affected by outliers than mean. A single extreme value doesn't skew.
• Outlier investigation: why is one peer trading at a very different multiple? Unique factor, or error?

Applying to Target

• Select appropriate multiple: median, mean, or specific peer if very similar.
• Apply to target's metrics: Target Value = Target Metric × Comparable Multiple.

Example: Peers trade at median 10x EV/EBITDA. Target EBITDA = $50M. Target EV = $50M × 10 = $500M. Equity value: EV - Net Debt + Cash = Equity Value. Divide by shares = per share.

Adjusting for Differences

• Growth adjustment: if target grows faster than peers, it deserves a premium. Use PEG ratio (P/E / Growth) for comparison.
• Margin adjustment: higher margin → higher multiple. Regression analysis can quantify the relationship.
• Risk adjustment: higher risk → lower multiple. Consider leverage, business risk, geographic exposure.
• Size adjustment: smaller companies often trade at a discount (liquidity, risk). Apply a small-cap discount if appropriate.

Multiple Ranges

• Present range: don’t rely on a single point estimate. Use the 25th-75th percentile of comps for a value range.
• Football field: visual showing valuation ranges from different methods (comps, DCF, transactions). Consensus where ranges overlap.

Common Pitfalls

• Wrong comparables: selecting peers that aren’t really similar. “Tech” is broad — SaaS vs hardware very different.
• Ignoring differences: applying the median without adjusting for growth, margin, risk differences.
• Circular reasoning: if the market is overvalued, comps will give an inflated value. Comps reflect market, not intrinsic value.
• One-time items: using reported instead of normalized metrics distorts comparison.

Normalized Multiples

• Adjust EBITDA: for one-time items, non-recurring costs. Use “adjusted EBITDA” if disclosed.
• Stock-based compensation: material for tech. Include or exclude consistently across peers.
• Acquisitions: recent acquirers may have depressed earnings. Normalize for integration costs.

Precedent Transactions

• Similar analysis using M&A transactions instead of trading multiples. What acquirers paid for similar companies.
• Premium: transaction multiples typically higher than trading (control premium).
• Compare to trading with awareness of premium.
• Limitations: transactions may be dated, different market conditions. Smaller sample than trading comps.

Integration with DCF

• Cross-check: DCF and comps should give similar values if assumptions are consistent. Divergence signals issues.
• If DCF >> Comps: DCF assumptions too optimistic? Or is the market undervaluing peers?
• Triangulation: use both methods, understand differences, form a balanced view.