pitch-comps

name

pitch-comps

description

Build a Bloomberg / CapIQ-style comparable companies set for a subject ticker. Identifies peers via a curated override map (with correlation and SIC fallbacks), pulls current multiples (EV/Sales, EV/EBITDA, P/E) for the subject and peers, renders the comp table with median / mean / 25-75 percentile summary stats, runs a regression-adjusted multiples view that controls for growth and profitability, and surfaces a one-line banker read at the bottom. Use when an analyst or banker is preparing pitch materials, a fairness opinion, or a valuation memo. Requires Stocks Starter for financials.

pitch-comps

You hand over a subject ticker. The skill identifies peers using the same three-layer methodology as earnings-drilldown's peer-reaction (curated override → correlation → SIC fallback), pulls TTM revenue, operating income, EPS, and balance-sheet items for the subject and the peers, computes current multiples (EV/Sales, EV/EBITDA, P/E) plus growth and margin metrics, summarizes the peer set with median / mean / 25-75 percentile bands, runs a regression of each multiple on growth and margin to compute a peer-implied multiple for the subject, and emits a one-sentence banker read.

The output drops into a pitch deck or fairness-opinion appendix unchanged. The structure matches what bankers already read in Bloomberg's RV (relative value) screen and CapIQ's comp set page.

When to invoke

• A banker is building a comp page for an MD's pitch deck
• An analyst is writing a coverage initiation and needs a valuation table for the subject vs. its peer set
• A buy-side PM is sizing a position and wants to know "what would the subject's multiple look like if peers traded it"
• The user says "build comps for $TICKER", "where does $TICKER trade vs. peers", "is $TICKER cheap or rich on multiples"

What you need

• A subject ticker (CRM, NVDA, etc.)
• MASSIVE_API_KEY exported in the environment
• Stocks Starter plan minimum. The full peer fetch fans out 9+ ticker-details and 9+ financials calls; on free Basic (5/min) this will run but will take ~5 minutes. Starter (unlimited) finishes in under 30 seconds.

What you get back

Two output layers from one analysis.

Layer 1: canonical JSON matching output-schema.json. Subject metadata, peer list with each peer's multiples and metrics, summary statistics (median / mean / p25 / p75 per multiple), the regression-adjusted block (per-multiple implied vs. actual and discount / premium), the one-sentence read, and the source endpoints with fetched-at timestamps. UIs, downstream agents, and Python scripts consume this.

Layer 2: rendered comp table in Bloomberg RV / CapIQ comp page style. See references/rendering.md. Subject highlighted at the top, divider, peers, divider, summary stats, optional regression-adjusted section, mandatory one-sentence read at the bottom.

How it works

1. Peer selection per references/peer-selection.md. Three-layer waterfall: curated override map first (covers the top ~30 US large-caps where SIC misclassifies), correlation-based for uncurated names, SIC fallback last. Records the selection method in the JSON so the consumer knows the peer-set quality.
2. Pull current price + market cap for the subject and each peer. One snapshot call and one ticker-details call per name. Compute enterprise value per references/multiples-methodology.md as market_cap + total_debt - cash + operating_leases + minority_interest. total_debt and cash are required; if either is missing for a name, EV-based multiples are emitted as null and the missing field is recorded on the per-name ev_components audit trail.
3. Pull TTM financials for the subject and each peer: revenue, operating income, depreciation and amortization (often null on software comps, see methodology), net income, diluted EPS. Compute revenue growth TTM vs prior TTM and EBITDA margin per references/growth-and-profitability.md.
4. Compute multiples: EV/Sales, EV/EBITDA, P/E. Per peer. null where the inputs aren't available (e.g. SAP's ADR financials gap, or a peer with negative EBITDA where the multiple is meaningless).
5. Cohort statistics per references/cohort-statistics.md: median, mean, 25th and 75th percentiles, computed over peers only (subject excluded so the comparison is honest). Drop nulls from the summary; never impute zero.
6. Regression-adjusted multiples per references/regression-adjustment.md. Fit multiple ~ growth + ebitda_margin across peers with numpy least-squares. Predict the subject's multiple given its own growth and margin. Compare to actual; surface as a discount or premium. Flag when n_peers < 8 because the regression's degrees of freedom get tight.
7. Generate the read. One sentence keyed off the regression output: where the subject sits vs. peers on growth-adjusted multiples and which multiple drives the divergence. Banker-tone, no hedge words, no "mispriced upside."

Foundations used

• massive-api-patterns for REST auth, rate limiting, and the 4-step lastTrade.p → min.c → day.c → prevDay.c snapshot fallback chain. FMV is not a field on the v2 snapshot response and is not in this waterfall.

Output mode: table

Same table mode as universe-builder. The format follows the canonical table rules in universe-builder/references/rendering.md with the comp-set-specific overrides in references/rendering.md: subject row at top, divider, peer rows, divider, summary stats rows, optional regression-adjusted block, mandatory one-sentence read.

A custom UI consumes the JSON and renders a sortable grid with hover-to- inspect on each cell's source endpoint. Claude Code users read the rendered table.

Endpoints used

• GET /v3/reference/tickers/{ticker}: subject and per-peer ticker details. Market cap, sector, shares outstanding, name. One call per name.
• GET /v2/snapshot/locale/us/markets/stocks/tickers/{ticker}: current price for each name. Cheap; falls back through the 4-step lastTrade.p → min.c → day.c → prevDay.c waterfall per the API patterns foundation.
• GET /vX/reference/financials?ticker={ticker}&timeframe=quarterly&limit=8&order=desc: eight quarters of financials per name. TTM revenue = sum of the most recent 4 with revenue data, prior TTM = sum of quarters 5-8 for the growth calculation. Operating income, D&A, net income, diluted EPS. Balance sheet for long-term debt.
• Optional: GET /v2/aggs/ticker/{ticker}/range/... when correlation- based peer selection is needed (uncurated subject tickers).

Verify endpoint paths against current docs at massive.com/docs before shipping; field names and versions shift.

Doesn't handle (yet)

• D&A often missing for software comps. EBITDA in v1 is computed as operating_income + d_a_or_zero and labeled as such in the schema. For software peers where D&A isn't broken out, the "EV/EBITDA" multiple is closer to EV/EBIT. Documented in multiples-methodology.md.
• Preferred stock is not added to EV. The balance sheet endpoint doesn't expose it as a separate field; the simplification is documented and is a clean PR extension. Minority interest IS pulled via _first_non_null against noncontrolling_interest / redeemable_noncontrolling_interest.
• P/B and P/FCF are out of scope for v1. P/B requires book value per share (derivable from equity / shares); P/FCF requires true FCF which is the same blocker as the operating-cash-flow-yield work in universe-builder.
• Forward multiples (NTM EV/Sales, NTM P/E) require consensus estimates. Benzinga has analyst ratings but not consensus estimates in the bundle currently subscribed; this is a Tier A extension when consensus is available.
• Foreign issuer ADRs like SAP, ASML, NVO often have empty financials in Massive's endpoint. The skill keeps these in the peer set with null multiples (they still inform the "Salesforce trades vs which peers" framing) but they don't contribute to summary stats or the regression.

These are clean PR extensions. The output schema reserves space for each so adding them later doesn't break consumers.