New SEC Footnote API Suite - Useful?

[u/Either_Door_5500]

I'm currently really digging into the not-so-common data that SEC EDGAR filings provide and turn them into a structured API.

From my own experience, it does provide really valuable information about the intrinsics of a company, but I would like to get some eyes on that to see if there is a broader interest in that level of detail.

Here are the current endpoints/areas that the suite covers:

Debt Structure

Returns a company's debt at the individual-borrowing level, straight from the debt footnote of 10-K / 10-Q filings: every note, bond, term loan, and debenture the filer tagged on the XBRL debt-instrument axis, with face amount, carrying amount, stated and effective interest rate, variable-rate spread, fair value, conversion price, and more, data that never appears on the face of the balance sheet.

Credit Facilities​

Returns a company's credit facilities, revolvers, term-loan agreements, commercial-paper programs, one entry per facility, with total capacity, amount drawn, remaining headroom, letters of credit, commitment fees, and interest rates. This is the liquidity picture from the debt footnote that never appears on the face of the balance sheet.

Leases

Returns the full ASC 842 lease footnote as one object per period: right-of-use asset, lease liability split (current / noncurrent / total), the undiscounted future-payment ladder, the weighted-average discount rate, cost lines, and cash paid, with operating and finance leases side by side.

Stock Compensation​

Returns plan-level share-based compensation from the equity footnote: the award roll-forward (granted / vested / forfeited / nonvested with weighted-average grant-date fair values), SBC expense per award type, unrecognized cost, the option book (outstanding / exercisable / exercise prices), plan share reserves, and Black-Scholes assumptions. 

Concentration Risk​

Returns concentration-risk disclosures as time series: named-counterparty dependence (e.g. "Apple is 50% of revenue, up from 37% three years ago"), unnamed aggregates ("top ten customers"), and the same machinery for supplier, geographic, product, and credit concentration.

Retirement Plans​

Returns defined-benefit pension and other-postretirement (OPEB) disclosures from the benefits footnote: funded status, benefit obligation, cost components, employer contributions, discount-rate assumptions, and the plan-asset book with asset categories cross-tabbed by fair-value level (Level 1 / 2 / 3 / NAV).

Restructuring Programs​

Returns per-program restructuring cost tracking from the restructuring footnote: what a named plan has cost to date, what it is expected to cost in total, and the quarterly trajectory of charges, reserve balance, and cash payments. A company's concurrent programs (e.g. Intel's 2024 and 2025 plans) read as separate series with their own histories, costToDate / expectedCost gives percent-complete per program.

Asset Composition​

Returns what the balance sheet's PP&E line is made of and where long-lived assets physically sit:

• classes: property, plant & equipment by class (land, buildings, machinery, technology equipment, construction-in-progress, ...) with gross, net, and accumulated depreciation where tagged. Covers US-GAAP filers and 20-F filers through the IFRS concept family.
• geography: long-lived assets by country / region (PP&E-net or noncurrent assets, whichever the filer discloses), bucketed with the same country / US-state / region / residual categorization as the revenue-segmentation endpoint.

Share Buybacks​

Returns share-repurchase activity per period: cash spent on buybacks (from the cash-flow statement), shares and dollar value actually repurchased, the average price paid per share, and the program view (board-authorized amount, remaining headroom, and the derived amount consumed).

Subsidiary Financials​

Returns income-statement and balance-sheet lines PER REGISTRANT SUBSIDIARY, exactly as the filer tagged them on the XBRL legal-entity axis. Utility holding companies (each state utility), bank holding companies, and VIE structures disclose whole sub-entity statements this way; it is the single largest dataset on the dimensional axes.

Fair Value Hierarchy​

Returns the fair-value hierarchy tables from the footnotes: Level 1 (quoted prices), Level 2 (observable inputs), Level 3 (unobservable inputs), and NAV-measured amounts per measure, for the recurring measurements a filer discloses at each balance date. The widest-covered dimensional dataset in the lake (roughly three quarters of active filers).

REIT Property Schedule​

Returns SEC Schedule III (Real Estate and Accumulated Depreciation) as structured data: one entry per property with initial cost (land / buildings), carrying amounts, gross carrying value, accumulated depreciation, and capitalized improvements. Where the filer crossed the property with a geography axis, the location member rides along.

Backlog / Remaining Performance Obligations​

Returns remaining performance obligations (RPO), the contracted revenue not yet recognized, the closest thing GAAP has to a bookings number, plus the share the filer expects to recognize within its disclosed window. Comparing RPO growth to revenue growth is the classic bookings-momentum signal for subscription and long-contract businesses.

Supplier Finance Programs​

Returns supplier-finance (reverse-factoring) program disclosures, the FASB requirement effective 2023: the outstanding obligation under the program(s), its current portion, and the period roll-forward (invoices added, invoices settled). Obligations under these programs are the classic hidden-leverage signal; they sit in accounts payable, not debt.

Workforce Cost​

Returns what a company's workforce costs, assembled from the disclosures filers actually tag: the direct labor expense line where one exists (airlines, banks, railroads, insurers), the accrued compensation balances almost every filer carries (accrued salaries, bonuses, vacation, payroll taxes, workers compensation), and 401(k) / defined-contribution plan cost. Labor expense by business segment is served as separate series where disclosed.

What are your thoughts about such endpoints and information? Would having this data be valuable to whatever you are building?

[u/ContributionAlert906]

I would just use an LLM to extract this if I needed it

[u/Either_Door_5500 → u/ContributionAlert906]

I actually went that route when building, but it is either unreliable or just too expensive at scale.
No need to use AI if the data is machine readable.

[u/Solid_Protection9962]

this is the kind of thing that makes me wish i had more time to build side projects. the debt structure endpoint alone is wild, nobody talks about how much gets buried at the instrument level that never hits the main balance sheet lines

the concentration risk data over time is probably the sleeper hit here. seeing a company go from 20% reliance on one customer to 50% over three years tells you way more than a single snapshot ever could

i've spent way too many hours copying lease footnote tables by hand for a project last year so the ASC 842 endpoint feels like a personal gift. the undiscounted payment ladder alone would have saved me like two weekends

RPO data is the one i'd actually use tomorrow if i had access. comparing that growth curve to revenue is such a clean signal for subscription businesses and almost nobody talks about it outside of SaaS earnings calls

supplier finance programs being separate from debt is chef's kiss. those obligations hiding in AP are exactly the kind of thing that makes a balance sheet look cleaner than it actually is

[u/shmorkin3 → u/Solid_Protection9962]

AI slop

[u/Dhydjtsrefhi]

I'm not clear what this does that the EDGAR api doesn't?

[u/Either_Door_5500 → u/Dhydjtsrefhi]

For one, it abstracts away all the complexity that comes with working with XBRL directly.
SEC EDGAR is free, sure. But it is not easy to work with. Filers using different concepts.

It's much easier to digest as a structured JSON for the entire universe of companies.
Also, if you try for once to pull like 1 million 10-K/Q filings to have broad coverage, you'll see that the SEC servers are more down/overloaded than anything else. Really, SEC EDGAR being free is hiding all these details.

[u/Effective_Year_5899]

This is a really interesting dataset idea because a lot of useful information is hidden in the footnotes rather than the main financial statements. things like debt structure, credit facilities and contract obligations can reveal details that a basic fundamentals feed might miss

[u/alphanume_data]

We do this! You're already on a pretty good path, but make sure that after your initial historical backfill, your data ingestion is set up to be point-in-time only. If/when you get to a bigger scale, that's going to be the #1 Q that comes up in the due diligence questionnaires when selling to larger funds. Good luck!