Trading Comparables: Build the Peer Set and Choose the Right Multiple
When Honasa Consumer, the parent of Mamaearth, came to the Indian public markets, the valuation debate was not just “what multiple?” It was “comparable to whom - mature FMCG giants, digital-first beauty brands, or loss-making internet companies?” That one judgement call can move a valuation more than any spreadsheet formula.
- Trading comparables value a company using how similar listed companies are currently priced by the market.
- The two real decisions are: build the right peer set and choose the right multiple.
- A good peer is similar in business model, revenue drivers, geography, growth, margins, capital intensity and risk - not merely the same industry label.
- Always match numerator and denominator: EV goes with pre-debt operating metrics like EBITDA; equity value goes with post-debt metrics like PAT or EPS.
- Use forward multiples when the business is changing fast; use LTM multiples only when current earnings are representative.
- Prefer the median over the mean, remove true outliers, and explain why your chosen range is defensible.
- The biggest trap: applying an average peer multiple without checking business quality, accounting differences and capital structure.
Trading comps are a market mirror. You are not trying to discover an absolute “true value” from first principles; you are asking, “How is the market valuing similar businesses today, and where should this company sit in that range?”
Core Explanation: How Trading Comparables Actually Work
Trading comparables value a target company by applying valuation multiples observed for similar publicly traded companies to the target’s financial metrics.
The method feels simple: find peers, calculate their multiples, apply a selected multiple to the target. The difficulty is that “similar” is not a database filter. Similarity has to be argued through the business model and the economics.
Step 1: Build the Comparable Company Set
Start broad, then narrow. A strong comps set usually has primary peers and secondary reference peers. Primary peers are close enough to drive the valuation range. Secondary peers are useful context but should not mechanically drive the multiple.
For an Indian D2C beauty and personal care company, simply comparing it with HUL or Dabur may overstate maturity and understate digital growth. Comparing it only with internet companies may ignore brand gross margins and repeat purchase economics. The strategic “so what”: the peer set should reflect both category economics and go-to-market model, not just the sector name.
Step 2: Clean the Numbers Before You Calculate
Before calculating multiples, ensure the numerator and denominator are comparable. Most bad comps are not mathematically wrong; they are definitionally inconsistent.
Step 3: Choose the Multiple That Matches the Business
The right multiple depends on what drives value in the business. A bank, a SaaS company, a cement manufacturer and a fashion retailer should not be valued with the same primary metric.
Definitions You Should Be Able to Say Cleanly
- Relative valuation: estimating value by comparing a company with how similar assets are priced in the market.
- Trading comparables: a relative valuation method using valuation multiples of similar publicly listed companies.
- Enterprise value: the value of operating assets available to all capital providers, usually equity value plus net debt and other claims.
- Equity value: the value attributable to common shareholders after debt and other senior claims.
- Valuation multiple: a ratio that links market value to a financial metric such as EBITDA, earnings, sales or book value.
Worked Example: EV/EBITDA Without the Outlier Trap
Assume you are valuing a target with EBITDA of ₹120 crore. Four listed peers trade at EV/EBITDA multiples of 11x, 13x, 15x and 28x. The 28x company is a high-growth outlier with a different business model.
If you had used the simple mean including the 28x outlier, the multiple would be 16.75x and the valuation would be materially higher. That is why peer selection is not housekeeping - it is valuation judgement.
Case Study: Honasa Consumer and the Peer-Set Problem
Honasa Consumer, known for Mamaearth, showed why valuing a digital-first consumer brand depends heavily on whether you treat it like FMCG, beauty retail, or a new-age platform.
Situation: Honasa Consumer went public in India in 2023 after building brands in beauty and personal care through digital channels and omnichannel expansion. The valuation conversation was difficult because India had few perfect listed comparables for a scaled, digital-first, brand-led beauty company.
The move: A sensible comps approach would not blindly use only large FMCG companies or only new-age internet businesses. Large FMCG players bring brand, distribution and margin context. Digital and beauty-focused peers bring growth, online acquisition and category context. The analyst’s job is to separate primary peers from reference peers, then select a multiple that reflects current profitability and expected operating leverage.
Outcome or lesson: The primary driver of the valuation debate was peer-set ambiguity: the company had consumer-brand characteristics but also digital acquisition and portfolio-building characteristics. Supporting drivers included growth expectations, margin trajectory, offline distribution expansion, brand concentration and customer acquisition efficiency. The lesson for interviews is clear: when exact peers do not exist, do not force precision - build a tiered peer set and explain the trade-offs.
The strategic takeaway: in trading comps, the peer set is not a supporting appendix. It is the core of the valuation argument.
How AI Changes Trading Comparables
AI does not remove valuation judgement, but it changes how quickly you can build and challenge a comps set.
- Peer discovery becomes broader: AI tools can scan annual reports, investor presentations and business descriptions to identify companies with similar revenue models, not just the same exchange classification.
- Adjustment work becomes faster: LLMs can summarize footnotes on exceptional items, leases, segment revenue and accounting changes. You still verify the source before using the number.
- Multiple selection becomes more evidence-backed: AI can compare why some peers trade at premiums - growth, ROE, margins, leverage, governance, scarcity value - and help you avoid one-factor explanations.
Use Perplexity to identify listed Indian and global peers with source links, then load the target company annual report and 3-5 peer annual reports into NotebookLM. Ask: “Create a peer-set table comparing business model, geography, revenue growth, EBITDA margin, leverage and best valuation multiple. Flag weak comparables.” Then verify every number from the original filing before using it.
Interview Relevance
“You are valuing an Indian D2C consumer company with no perfect listed peer. How would you build the trading comps set and choose the right multiple?”
Always add one sentence on why the target deserves a premium or discount: faster growth, better margins, stronger ROIC, higher leverage, weaker governance, smaller scale or greater customer concentration.
Common Mistake
The mistake that costs candidates is choosing peers by industry label and market cap, then applying the average multiple mechanically. It fails because valuation multiples reflect growth, margins, risk, accounting and capital structure - not just sector membership. One-line fix: build a tiered peer set, use median or range, and justify the target’s premium or discount with fundamentals.
What to Revise Next
Once trading comparables are clear, move to deal-based and break-up valuation methods. Revise these next: