Trading Comparables

Use This Skill When

• The user wants a relative valuation view grounded in an explainable peer set.
• The task is to benchmark a company against peers on growth, margins, capital intensity, and quality, not just headline multiples.
• Relative valuation is more decision-useful than a standalone intrinsic valuation for the question at hand.
• The output should explain premium or discount drivers rather than just display a table.

Required Inputs

• target company and, if available, ticker
• candidate peer set or enough business context to construct one
• valuation metrics: EV/Sales, EV/EBITDA, EV/EBIT, P/E, FCF yield, or other relevant metrics
• operating metrics: growth, margins, returns, cash conversion, leverage, capital intensity
• period basis: LTM, NTM, FY1, FY2, or calendarized estimates
• business model qualifiers: recurring vs transactional, vertical exposure, region, size, cyclicality
• If a full comp data set is unavailable, use a smaller but defensible group, disclose missing metrics, and avoid pseudo-precision on relative valuation gaps.

Workflow

1. Build the comp set from business similarity first, then explain inclusions and exclusions.
2. Normalize the comparison basis:
   • same forward period where possible
   • similar accounting basis
   • note cyclical or temporary distortions
3. Compare the target and peers on the metrics that actually drive deserved multiples:
   • growth durability
   • margins and returns
   • balance-sheet quality
   • capital intensity
   • valuation multiples
4. Explain why the target should trade at a premium, parity, or discount.
5. Separate structural differences from temporary dislocations.
6. Conclude with a relative valuation range and the key variables that could move it.

Output Requirements

• Justify the comp set before using it.
• State whether each metric is historical or forward and keep the basis consistent.
• Explain premium or discount drivers using business quality, not only multiple arithmetic.
• Flag distorted metrics caused by one-time items, cyclicality, or segment mix differences.
• Use Fact, Assumption, and Inference labels when valuation conclusions extend beyond observed data.

Output Template

Quality Checks

• Verify every peer is relevant on business model, customer base, or economic profile.
• Check that all valuation metrics are on a consistent period basis.
• Confirm that premium or discount claims cite at least one operational reason.
• Ensure temporary distortions are identified before concluding a stock is cheap or expensive.
• Remove any peer whose inclusion cannot be defended in one sentence.

Guardrails

• Do not compare incompatible business models without explaining the adjustment or limitation.
• Separate facts, assumptions, and inferences explicitly.
• Do not rely on multiples alone; explain why multiples should differ.
• Do not invent consensus estimates, NTM margins, or market-implied valuations.
• Avoid false precision when the peer set is small or heterogeneous.

Example Prompts

• Build a trading comparables analysis for this company and explain its premium or discount.
• Compare this stock to peers on growth, margins, and valuation multiples.
• Select a defensible comp set, show whether the stock's multiple is deserved, and explain the key sensitivity.