Trading comps are market benchmarks that compare a company’s valuation and operating metrics to a defined peer set of public firms. Deal comps are benchmarks built from announced or closed transactions to compare valuation, structure, and terms. The point is a defendable range that reflects what markets are willing to pay for assets with similar drivers and constraints.
The payoff for getting comps right is simple: you get credible ranges that survive diligence, committee review, and audits. The path to that payoff starts with data provenance and continues with clear policies, consistent adjustments, and documents to support every key number.
What this guide covers and why it matters
The objective is to produce comps that hold up under real scrutiny. That means using sources with clear lineage, fast updates, and policies that remain stable under stress. This review covers paid platforms and free sources for equity, credit, and private markets. It highlights where each tool is strong, where it struggles, and what to pair for practical workflows so you can build packs that finish the job, not just start it.
The scope includes trading comps for public equities and credit with an emphasis on universe definition, metric normalization, capital structure and enterprise value reconciliation, calendarization and FX, and NTM or forward metrics from consensus or internal forecasts. It also covers deal comps for M&A, carve-outs, minority deals, PIPEs, debt offerings, and restructurings. Here the focus is EV versus equity value, cash-free debt-free conventions, minority interests, earn-outs, synergy framing, and whether to rely on announced or final terms. Valuation math such as DCF and LBO cases is out of scope except where it dictates data needs, and portfolio monitoring is excluded beyond comp inputs.
The best paid platforms for trading and deal comps
Bloomberg Terminal: The fastest real-time engine
Bloomberg delivers real-time pricing, broad equity and credit coverage, detailed capital structures including convertibles, preferreds, and leases, EV calculators, document retrieval, and broker research where entitled. For credit, primary and secondary pricing, curves, and CDS triangulate implied quality, which makes it the go-to when speed matters.
However, deal terms outside filings can be thin, private market depth is still secondary, and field-level construction requires diligence that introduces audit risk. Pricing is reported around $27,660 per user per year as of Nov-2023, and redistribution of raw data is restricted, which raises both cost and compliance considerations.
LSEG Workspace with Deals Intelligence: Long-run transaction depth
LSEG’s Workspace and Deals Intelligence provide deep M&A and new-issue databases, long history, structured metadata, and cross-border breadth. It is a primary engine for deal comps and syndicate analytics when you need consistent history across decades.
Gaps include lags for smaller or private deals, manual document pulls, and definitions for premiums and EV that require field-level confirmation. You will gain depth but should budget time for validation.
S&P Capital IQ Pro: Fundamentals tied to filings
Capital IQ’s strengths are fundamentals tied to filings with reconciliation notes, point-in-time histories, granular segments, and standardized EV and per share math. U.S. private-company coverage is robust and expanding globally, which boosts auditability for trading comps.
It is not focused on real-time market data, and some private financials are modeled or press-sourced and require validation. Use it for trading comp fundamentals, segment screens, and defensible data chains that you can trace back to filings.
FactSet: Governance and scale for larger teams
FactSet offers deep fundamentals, portfolio analytics, point-in-time estimates histories, transcripts, flexible screening, and detailed capital structure models. Data governance features work well across larger groups that need consistency and permissions at scale.
Deal coverage lags LSEG SDC for the longest history, and credit instrument depth varies by region. Use it when your trading comps need integrated governance and collaborative workflows.
Consensus estimates: Cleaner forward metrics
Visible Alpha and similar services parse broker models to improve NTM and forward metrics. They reduce non-GAAP drift and footnote mismatches by standardizing underlying definitions. Entitlement and redistribution limits apply. Keep the audit trail back to source broker models when adjustments are material to comply with policy and calm committees.
Credit and leveraged finance platforms: Terms change the price
S&P LCD, LevFin Insights, Debtwire, 9fin, and Reorg with Covenant Review provide pricing for leveraged loans and high-yield bonds, detailed terms, covenant analysis, and documents where available. These are the sources for OID, call protection, EBITDA add-backs, portability, MFN exceptions, and trends across credit packages. They are essential when your debt comp is only as good as the protections and definitions in the fine print and they complement hands-on covenant modeling.
Most 144A and private-credit documents are not public, coverage varies by region, and newer entrants have shallower history. Use them for LBOs, refinancings, amend-and-extends, and restructurings and treat the full term sheet as part of the comp.
Private markets platforms: Deal flow context and buyer maps
PitchBook and Preqin track fundraising and deal data across PE, VC, and infrastructure, including private company rounds, investor profiles, and buyer landscapes. They are solid for sizing, sector multiples, and sponsor outreach planning.
Coverage is often self-reported or press-driven and private financials can be partial. Validate with filings, registries, or management. For PIPEs specifically, pair with a refresher on PIPE investments to align structure and disclosure expectations.
Free and “good enough” sources that pass audits
SEC EDGAR and APIs: Ground truth for U.S. issuers
EDGAR is the bedrock for 8-Ks, S-4 or DEFM14A, 10-K or 10-Q, and 13D, 13G, or Schedule TO. For ABS, ABS-EE offers pool-level data. EDGAR APIs and bulk feeds enable machine workflows at zero cost. Treat filings as ground truth and reconcile platform discrepancies back to the source for auditability.
Company investor relations: KPIs and bridges
Investor relations sites post press releases, decks with KPIs, non-GAAP bridges, and webcasts that often disclose pro formas. Archive PDFs and webcasts with timestamps and reconcile deck figures to filed numbers to manage optics and accuracy.
FINRA TRACE and MSRB EMMA: Pricing and liquidity context
TRACE and EMMA are essential for corporate bond and muni trade prints, official statements, and historical trade activity. Free access is often enough for directional pricing and liquidity screens. Advanced analytics and deeper history may require paid redistribution, so use raw prints to confirm liquidity and price context.
Registries and non-U.S. portals: Cross-border facts
UK Companies House offers free filings and an API for private-company accounts, ownership, and charges. Canada SEDAR+ is the official filing portal for Canadian issuers and M&A circulars. EU ESEF and ESMA FIRDS or FITRS provide iXBRL-tagged annuals, instrument reference, and transaction reporting data. Add local competition authority decisions to document market definitions and closing conditions when doing cross-border M&A considerations.
Macro and reference data: Normalize what you compare
St. Louis Fed FRED and Treasury publications provide rates, inflation, discounting series, and currency context. Use them for cost of capital assumptions, calendarization, and currency normalization so you compare like with like.
Build trading comps that survive diligence
Define the universe by business drivers
Start with revenue mix, margins, end markets, channels, regulation, and capital intensity. Exclude pre-revenue names, distressed outliers, state-owned firms with non-economic goals, and float-constrained controlled companies unless they are central to the case. When in doubt, show a core set and a secondary set to separate signal from noise.
Normalize metrics and EV consistently
Construct EV as diluted market cap plus net debt, preferreds, minority interest, and other debt-like items minus non-operating assets. Confirm finance leases, pensions, tax receivable agreements, and earn-out liabilities. Decide a lease policy under IFRS 16 or ASC 842 and apply it consistently. Use the treasury stock method for options and RSUs and apply if-converted treatment for in-the-money converts that are not net share settled. Fix canonical EBITDA and EPS definitions across the set and align for stock-based compensation, restructuring, and leases. Adjust consensus to match that policy so the output is apples to apples.
Calendarize and convert currencies
Calendarize LTM and forward figures for mixed year-ends. Convert to one currency using period-appropriate average or spot rates based on your policy. Note the basis in footnotes to avoid rework later.
Control quality like an auditor
Timestamp every snapshot and keep links to filings. Log changes and document the page number for any adjustment that moves valuation. A tight control loop beats memory when the committee asks where a number came from. If you need layout guidance, see a typical public trading comps page structure.
Build deal comps that reflect real terms paid
Define deal value with precision
Equity value is the headline. EV adds net debt, preferreds, minority interest, and other debt-like items as of the deal date, adjusted for transaction mechanics. In cash-free debt-free deals, confirm working capital targets, debt-like items, and any debt financed at close. These choices swing multiples, so cite the merger agreement or S-4 when you make them.
Pick the right date basis
Use announcement date for market-condition comps. Use closing date when you want what was actually paid. For premiums, reference the close price one day or one week pre-announcement per your source’s definition and apply it consistently.
Separate synergies and pro formas
When synergies or pro formas appear in an S-4 or DEFM14A, show both pre-synergy and pro forma multiples. Do not mix synergy-adjusted EBITDA from targets with unadjusted trading comps. Keep the sets clean and parallel.
Include financing terms as part of the comp
For LBOs and recaps, the financing package is part of the price. Capture margin, OID, call protection, covenants, capacity baskets, MFN, portability, EBITDA add-back caps, and free-and-clear amounts. Pull the documents and summarize gaps if they are not public. Reflect those terms in your sources and uses so sponsors can react to constraints early.
Map documentation so nothing is missing
For trading comps, pull 10-K, 20-F, or 40-F, the latest 10-Q or 6-K, earnings materials, investor day decks, and credit agreements if covenant EBITDA matters. For deal comps, gather 8-Ks, the merger agreement, S-4 or DEFM14A or DEFA14A, fairness opinion appendices, tender docs, press releases, competition decisions, and cross-border equivalents. The more you tie into filed sources, the less time you spend debating press snippets.
Compliance and governance that prevent rework
Most vendors restrict raw redistribution and derived databases for external use. Use filings in external materials and keep vendor usage compliant. EDGAR APIs are fair game, but many websites restrict scraping. Respect robots.txt and rate limits. Never use material nonpublic information in public comps and maintain restricted lists. Retain comp packs, source files, and decision logs per firm policy if you are regulated.
Choose a stack by use case and budget
For trading comps, pick the tool your team can audit to filings fastest. S&P Capital IQ Pro or FactSet handle fundamentals while Bloomberg or LSEG handle pricing and instruments. Choose based on who fixes data at 2 a.m. before committee. For deal comps, LSEG Deals Intelligence is the deepest single source. Bloomberg’s deal tools help, but you will pull more documents manually. For sponsor and private coverage, add PitchBook or Preqin. For credit comps, there is no single winner. Use Bloomberg for pricing and pair with LCD, LFI, 9fin, Debtwire, or Reorg for terms and documents. Your best platform is the one that gets you the indenture or credit agreement fastest.
If you must run free only, it is viable for U.S. public comps and selected debt comps if you accept slower workflows. EDGAR plus TRACE or EMMA plus IR and FRED can cover many needs, with Companies House, SEDAR+, and ESEF filling non-U.S. gaps. The trade-off is analyst time and uneven point-in-time snapshots.
Implementation timeline and controls
In weeks 1 to 2, set policies for EBITDA definitions, lease treatment, dilution, and EV rules. Assign owners for equity, credit, and private data so governance has names. In weeks 3 to 4, procure seats, entitle APIs, set SSO or MFA, and build baseline sector screens and locked templates for trading and deal comps. In weeks 5 to 6, map fields to sources and fallbacks, script EDGAR pulls, store PDFs or XBRL, and set TRACE or EMMA queries for common tickers and CIKs. In weeks 7 to 8, run two live case studies, red-team comp selections and adjustments, and test that you can recreate any pack point in time. Ongoing, run monthly policy checks, quarterly vendor audits, and annual renegotiations using usage and error metrics.
Run kill tests. If your key multiple relies on an EBITDA definition you cannot document from filings or management, drop it. If you cannot construct EV due to unresolved debt-like items or undisclosed non-controlling interests, show equity-only metrics or exclude. Standardize IFRS 16 versus pre-ASC 842 treatment, stock-based compensation adjustments, and joint venture economics and update stale debt pricing or flag illiquidity. Do not rely on press releases for EV when an S-4 shows adjustments. For private multiples, seek two independent sources or show ranges.
Practical recommendations by budget
At minimal cost, use EDGAR, TRACE or EMMA, FRED, Companies House or SEDAR+, and company IR. Build internal scripts and sheets. Expect higher analyst time and patchy international coverage. At mid-tier, add Capital IQ or FactSet for fundamentals, add PitchBook for private deal color, and consider LSEG Workspace for real time and deals if you do not have a Bloomberg seat. At full stack, combine Bloomberg plus Capital IQ or FactSet plus LSEG Deals Intelligence plus Reorg or 9fin plus PitchBook or Preqin. Integrate APIs into a versioned data store and wire outputs into your debt schedule and comp templates for audit-ready packs across equity, credit, and private markets.
Fresh angle: Measure “time to ground truth” to manage quality
A practical way to raise comp quality is to track two operating metrics. First, time to ground truth measures how long it takes to locate and archive the filed document that supports each key input, such as EV components, pro forma EBITDA, or credit covenants. Second, point-in-time coverage rate measures the fraction of your comp spreadsheet that is backed by a point-in-time source. Add a third metric called error escape rate to capture adjustments made after committee feedback. Teams that publish and manage these three metrics see fewer escalations and faster approvals because the process focuses on evidence, not format. As a rule of thumb, target under 30 minutes to ground truth for any single input and over 90 percent point-in-time coverage before a live committee review.
Conclusion
Trading comps live or die by definitions and capital structure accuracy. Deal comps live or die by documents and full terms paid. The right platform is the one that gets you to audit-ready numbers with the least rework under time pressure. Use paid tools for speed and breadth, but anchor numbers in filings and authoritative public sources. Avoid tool sprawl and spend where it removes the biggest friction: forward estimates, debt term details, and document retrieval. Close every project by archiving versions, users, and logs with a retention policy so you can recreate any comp pack on demand.