Trade-Credit & Supply-Chain Score
A monthly score and supporting attribution data on every covered public company — vendor payment cadence, trade-credit balance trajectory, supplier-concentration risk, supply-chain network deltas. Delivered via API and SFTP.
- Engagement
- Subscription · monthly score updates · API + SFTP delivery
- Built for
- L/S equity PMs · Fundamentals analysts
Vendor payment behavior and supply-chain health predict equity returns — companies that hold large trade-credit balances while paying invoices on time outperform their indices. The data is scattered across vendor systems and never assembled at fund scale.
What this is
A ready-made score for funds that want trade-credit and supply-chain intelligence layered onto existing fundamental or factor strategies, without owning the data infrastructure. Three dimensions per company per month:
- Vendor payment cadence. Aggregated days-payable-outstanding signal across the company's vendor base — sourced and normalized so funds can compare across sectors.
- Trade-credit balance trajectory. Balance trend over the trailing twelve months, classified for the "high-balance, on-time-paying" pattern published research has flagged as alpha-generating.
- Supplier-concentration risk. Concentration of revenue on critical suppliers; concentration of supplier dependence in critical geographies; delta-over-time on both.
How it's built
The score is built on top of the Alternative Data Signal Engine pipeline — the custom build serves the score, the score serves the funds that don't need or want the build. Vendor payment data sourced from third-party aggregators; supply-chain network data from shipping manifests and corporate disclosures. Point-in-time aligned at month-end.
What you get
- Monthly score per covered company.
- Attribution data — the underlying vendor and supplier deltas the score moved on.
- Five years of point-in-time historical data for backtesting.
- API and SFTP delivery.
- A monthly methodology note when the score-generating model shifts.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- Subscription · monthly score updates · API + SFTP delivery
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Hedge Fund Suite
Alternative Data Signal Engine
A production pipeline that ingests one or more unconventional datasets, normalizes against a fund-internal schema, and serves processed factor scores, back-tested signals, and event-time alerts to the research stack.
- Same suite · Hedge Fund Suite
Consumer Spending & Foot-Traffic Dashboard
Weekly dashboards combining anonymized card spend (US consumer) and foot-traffic (mapped to ticker via store-location databases) — earnings-window trend detection for the names and themes consumer funds trade.
What buyers ask about this one.
Why ready-made instead of custom build?
The dataset is shared. Funds buying this aren't trying to source a unique trade-credit feed; they want the score and the attribution, alongside whatever else they already trade on. Subscription is the right commercial shape for a shared dataset.
What's the universe?
US large- and mid-cap public companies — roughly 2,000 names. Coverage expansion to European mid-cap and select Asian names is on the roadmap.
How is this different from credit-rating data?
Credit ratings are about default risk. This is about operating behavior — does the company pay vendors on time, and is the balance behavior trending in a way that correlates with future operating performance. The published research suggests these signals are orthogonal to the rating-driven signals most funds already trade.
What's the historical depth for backtesting?
Five years of monthly history at launch, extending as the underlying data series ages. Point-in-time correct — vendor and balance data reflect what was knowable at each historical month-end, not retroactive corrections.
Pricing?
Tiered against universe scope and delivery cadence. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026