Public Trading Comps Page: Layout and Process for Investment Banking Pitchbooks

Trading Comps: Build Audit-Ready Comparable Company Pages

A trading comps model for investment banking is a simple tool with a serious job: it shows what the market is paying for a set of comparable public companies so you can infer a fair range for a target. The trading comps page is the one-page grid in a pitchbook that collects prices, capital structure, financials, and multiples for those peers at a specific timestamp. Think of it as a disciplined snapshot you could hand to a committee and say, “Recreate this result from these sources,” and they could.

This guide explains how to design, populate, and defend that page under real diligence. You will learn what belongs on the grid, how to avoid common traps, and how to tie every number back to public or licensed data. The payoff is credibility: a tight median and range that stands up in investment committee and fairness reviews.

Purpose and bar for inclusion

The comps page earns its keep by turning a messy universe into a usable median and range, then showing enough of the bridge that anyone can replicate the math. Bankers want persuasive but defensible ranges. Sponsors and lenders care about comparability, cyclicality, and downside. Issuers want peers that support their story. Therefore, the page must anchor to an as-of time and spell out definitions and adjustments. If you cannot audit it, do not use it.

A layout that stands up in committee

Use a clean, left-to-right flow. If you have to shrink font below legible print, you are carrying too much. The following structure works when time is short and scrutiny is high.

Header essentials: Include a title with target and sector taxonomy, the as-of timestamp tied to close or a defined snap time, presentation currency, and sources. State the universe rule in one sentence.
Universe panel: List peers by ticker and exchange using the primary listing. Add sub-cohorts when helpful, such as Enterprise SaaS vs Payments, to anchor medians.
Market data block: Show price, 52-week range, average daily value traded when liquidity screens matter, and market cap.
Capitalization and EV bridge: Present diluted shares under treasury stock and if-converted rules, net debt, minority interest, preferreds, and associates or JVs. Define the enterprise value formula you apply.
Financials and multiples: Include LTM and NTM revenue, EBITDA, and EBIT. Add net income when P/E is relevant. Display EV/Revenue, EV/EBITDA, EV/EBIT, and P/E as the sector warrants. Add context lines like NTM revenue growth and LTM EBITDA margin.
Summary statistics: Display mean, median, and interquartile range. Mark outliers with a symbol. If space allows, include a small sparkline of medians over the last 12 months.
Notes and footnotes: State core definitions, lease treatment, FX rules, and pro formas. Attribute sources per license.

Process that survives diligence

Define the universe: Pick peers by business model, revenue mix, end-market, and margin profile, not just NAICS or GICS. Exclude microcaps or illiquid names unless they are true economic peers, and disclose any liquidity screens. Include conglomerates only if segment alignment is tight or you will run segment adjustments.
Fix the timestamp: Anchor prices and FX to a close or snap and record the market calendar. Use one primary source for price and shares. Keep estimates to a single vendor and a point-in-time vintage.
Build diluted shares: Start from basic shares and compute diluted shares under ASC 260 or IAS 33. Include in-the-money options and convertibles using the treasury stock method and if-converted, excluding anti-dilutive instruments. Monitor buybacks because trailing diluted shares can be stale within a quarter.
Bridge to EV: Use EV = price x diluted shares + total debt + preferreds + minority interest – cash ± justified adjustments. Define net debt, include borrowings and current maturities, and state whether restricted cash is excluded. Align associate treatment between EV and P&L.
Normalize and calendarize: Build LTM by rolling forward the latest quarters. Use one estimates vendor for NTM and calendarize fiscal years to a common end using simple proration and vendor growth rates. For conglomerates, use segment multiples or exclude from medians and footnote.
Align accounting conventions: Standardize EBITDA, including stock-based compensation treatment, across peers. For R&D-heavy sectors, decide up front whether to capitalize R&D for EBITA or NOPAT comparability, and avoid cross-border mixing when policies differ.
Keep currency coherent: Pick a presentation currency. Translate EV and financials at the same FX spot as the price timestamp or use average rates if you rebuild LTM from local filings. For ADRs, apply the correct ratio and align share count with the priced security.
Handle outliers: Mark negative-denominator multiples as NM and exclude NM from summary stats. Avoid silent winsorizing. Show the full range, compute medians excluding NM, and flag any deletions with a quantitative rule.
QA and sign-offs: Tie each figure to a source and date. Cross-foot EV. Refresh the morning of the meeting and stamp the version. Note material moves since print.

Mechanics you must get right every time

Equity value math: Use TSM for options and warrants, cross-check company-reported diluted shares, and adjust for post-quarter events. For convertibles, use if-converted logic if in the money or by convention when you add back interest to EBIT or EBITDA. Avoid double counting both debt and shares.
Enterprise value specifics: Measure preferred equity at liquidation preference or fair value. Add minority interest if EBITDA or EBIT is pre-NCI, and remove if you deconsolidate. Align associate income with EV by stripping either from P&L or EV.
Lease treatment: Match lease liabilities in net debt with the EBITDA basis you use. If you add lease liabilities, use post-IFRS 16 EBITDA. If you exclude them, use pre-IFRS 16 EBITDA that reflects cash rent.
Cash rules: Exclude restricted cash unless you can support near-term release. Do not net customer advances against cash.
Working capital financing: Identify receivables factoring and supply-chain finance. If material and peers differ, note or adjust to keep leverage apples to apples.
Pensions and other obligations: Add pension deficits only when material and peers share exposure. When in doubt, show a sensitivity instead of hard-adjusting EV.

Handling special cases

Negative EBITDA: Use EV/Revenue as the anchor. Add gross margin and rule-of-40 as context. Do not force EV/EBITDA into medians crowded with NM.
Financial institutions: Use P/TBV, P/E, and ROE and keep banks and insurers on their own grid.
Conglomerates: Mark them, exclude from medians unless you segment them, and keep a segment SOTP in the appendix.
SPACs and microcaps: Exclude non-operating vehicles. For de-SPACs, wait for two audited quarters post-merger unless the sector lacks peers. For context on vehicles and outcomes, see SPAC vs IPO.
FX volatility: Use hard-currency disclosures if available or exclude from medians and show separately. Translational noise can swamp the valuation signal.

Using the page in valuation work

Translate to a range: Pick the summary statistic that matches the company’s profile. In cyclical sectors, favor NTM medians and mid-cycle anchors. Cohort medians often beat all-in medians when growth and margin dispersion is wide.
Growth-adjusted checks: Where dispersion is high, test EV/EBITDA divided by NTM revenue growth or run a simple regression of EV/Revenue on growth and margin. If explanatory power is weak, skip the adjustment and stick to straight medians.
Triangulate methods: Keep the comps page clean and link it to your football field alongside DCF and precedents. For a refresher on triangulating tools, see these business valuation methods.
Downstream analysis: Feed the implied range into your DCF and your accretion-dilution analysis to test consistency with cash flows and EPS mechanics.

Implementation timeline and owners

Day 0-1 – Scoping: Associate and sector VP lock the inclusion rule and adjustments. Align with the client lead when the peer set is sensitive.
Day 1-2 – Data assembly: Analyst pulls price, FX, shares, debt, cash, and estimates from the chosen vendor set and timestamps each pull. Compliance confirms permissible use of any research-derived numbers.
Day 2-3 – Normalization: Build diluted shares, EV, LTM and NTM, and calendarization. Document lease and associate treatments. Draft footnotes as you go.
Day 3 – First review: Associate reviews coverage, flags outliers, and cross-foots. VP challenges inclusion and adjustments and decides on outlier handling.
Day 4 – Page build and QA: Analyst builds the grid, adds medians and interquartile ranges, and formats for print. A peer analyst independently checks against raw exports.
Day 5 – Sign-offs: VP signs off on medians and ranges for the football field. Compliance reviews attributions. Production locks the page with a version stamp.

Common pitfalls and quick tests

Share count drift: If buybacks or issuances exceed 2 percent in a quarter, do not trust vendor diluted shares without checking the latest filing.
Inconsistent EBITDA: If vendor EBITDA and EBIT do not tie to D&A, rebuild EBITDA and standardize stock-based compensation treatment.
Lease mismatches: If EBITDA is post-IFRS 16 while you exclude lease liabilities from net debt, EV/EBITDA is biased. Align the convention or use EV/Revenue for that cohort.
Associate double count: If associate income exceeds 5 percent of EBIT, adjust EBIT or EV to avoid double counting.
FX mapping errors: If ADR and local line imply different market caps after the ADR ratio and FX, fix the mapping before you compute EV.
Mixed estimate vendors: If more than one consensus vendor feeds the page, rebuild from one to avoid silent definitional differences in NTM.
Outlier deletion crutch: If you remove more than 10 percent of the universe to stabilize medians, your universe or metric is off. Re-scope the set or change the anchor multiple.
Stale or thin prices: If a name did not trade on the as-of day or spreads are stressed, exclude from medians and footnote.

Model discipline and audit trail

Structure matters: Store raw vendor pulls with timestamps and map tickers to source IDs. Avoid hard-coded numbers on the page. Keep all adjustments on one sheet with named ranges that footnotes reference. For broader modeling hygiene, build an audit-ready inputs tab.
Point-in-time integrity: Use point-in-time estimates locked to a single vintage date. Keep a PDF of the client version and the raw exports to guard against vendor backfills and restatements.
Licensing and attribution: Follow each vendor’s attribution rules. Mixing sources without proper credit creates distribution and compliance risk.

What belongs in the footnotes

Timestamp and currency: As-of date and time, FX source, and presentation currency.
Equity value build: TSM inputs, if-converted choices, share count source and date.
EV components: Net debt definition, lease treatment, and handling of minority interest, associates, and pensions.
Financial definitions: Whether EBITDA is company-adjusted or rebuilt, SBC treatment, and R&D capitalization choice.
Estimates vintage: Vendor name, vintage date, whether medians exclude NM, and the outlier rule.
Pro forma items: Closed deals or asset sales and their effective date.

Governance and compliance realities

Non-GAAP scrutiny: Keep company-specific adjustments visible but out of the median when they do not travel well across peers.
Data license hygiene: Respect vendor licenses and research separation rules. Use permitted datasets or clearly labeled internal models when allowed.
Retention discipline: Archive each page version, raw data exports, mapping tables, and Q&A. Hash the archive, apply document retention schedules, and honor legal holds.

A modern twist: automation and reproducibility

Teams can lift quality and speed by automating the data path and audit trail. Build the grid from query-driven tables, not pasted values. Use named ranges so footnotes pull definitions directly from a control panel. Store raw exports as CSV with timestamps and hash them to prove integrity later. Run a scripted cross-foot that checks EV and share counts against sources before printing. Finally, connect the comps output to your three-statement model and debt schedule so valuation scenarios and leverage tests update when medians change. When you push the range into a public-to-public merger model, you will save hours and reduce manual errors.

Final self-check

Recompute test: Do the displayed numbers let a reader recompute every multiple without hidden inputs?
Consistency test: Are currency, FX, as-of time, and estimate vintage explicit and consistent?
Medians and NM: Do medians exclude NM denominators and any removed outliers by a disclosed rule?
Second set of eyes: Has a peer re-pulled a subset and matched within rounding?
Defensibility test: Would you defend each adjustment in an investment committee or fairness workshop? If not, tighten it now.

Conclusion

A great trading comps page is not flashy. It is clear, reproducible, and anchored to definitions you can defend. If you define a tight universe, lock your timestamp and sources, align accounting, and document every choice, your medians will convince on their own. That is how you turn a one-page grid into a valuation backbone your deal team can trust.