{
  "data_version": "seed-0.1 · 2026-08-28",
  "source": "https://finance.foundation",
  "license": "CC BY 4.0",
  "collection": "research",
  "count": 6,
  "data": [
    {
      "id": "open-financial-data-stack",
      "title": "The Open Financial Data Stack, 2026",
      "category": "Infrastructure",
      "date": "2026-08",
      "summary": "Where open financial data actually stands: what is genuinely open, what is licensed, and where the gaps are for builders and AI agents.",
      "body": [
        "Financial data looks abundant but is structurally closed. Prices, fundamentals, filings, and reference data are dominated by a handful of vendors — Bloomberg, LSEG (Refinitiv), S&P Global, FactSet, Morningstar — whose terminals and feeds are licensed per seat or per use. The result is a paradox: the numbers that describe public markets are mostly not public in any machine-usable way.",
        "The genuinely open layer is thinner than it appears. Regulatory filings (EDGAR in the US, similar registries elsewhere) are open. LEI — the global legal-entity identifier — is open by mandate. OpenFIGI provides open instrument identifiers. Central banks and statistical agencies publish macro series (FRED being the gold standard of usable open data). Crypto is the outlier: its market data, on-chain activity and reference data are radically open compared to traditional finance.",
        "The gap is in the middle: clean, linked, entity-level data. Anyone can download a filing; almost no one can cheaply answer 'show me all listed subsidiaries of this group, with their tickers, exchanges and reporting currencies.' Entity resolution — knowing that Apple Inc., AAPL, US0378331005 and LEI HWUPKR0MPOU8FGXBT394 are the same thing — is where open data still loses to the terminals.",
        "This is the layer Finance Foundation is built around: not another price feed, but the connective tissue — entities, identifiers, relationships and definitions — published in a form that people, applications and AI agents can consume directly."
      ]
    },
    {
      "id": "global-exchange-landscape",
      "title": "Mapping the Global Exchange Landscape",
      "category": "Markets",
      "date": "2026-08",
      "summary": "A structural map of the world's stock exchanges: who owns them, how they concentrate, and why listing venues and trading venues are diverging.",
      "body": [
        "The world's equity markets are more concentrated than most investors realize. The two US exchanges — NYSE and Nasdaq — account for roughly half of global listed market capitalization. Add Shanghai, Shenzhen, Tokyo, Hong Kong, Euronext, LSE and India's NSE, and nine venue groups cover the overwhelming majority of world equity value.",
        "Exchanges themselves have consolidated into listed, for-profit groups: ICE owns NYSE; Nasdaq Inc. is itself Nasdaq-listed; LSEG earns more from data (Refinitiv) than from listings; Deutsche Börse and Euronext roll up national venues across Europe. The exchange business quietly became a data and clearing business with a trading venue attached.",
        "Meanwhile listing and trading have diverged. A company lists in one place but trades everywhere: ADRs in New York, fungible lines in Hong Kong, systematic internalizers and dark pools in Europe, 24-hour tokenized wrappers emerging offshore. 'Where does this stock trade?' now has a dozen correct answers — which is exactly why machine-readable market identifiers (MIC codes) and listing relationships matter more every year.",
        "India's NSE is the structural story of the decade: the world's largest derivatives exchange by contracts, riding a domestic retail investing boom. The Gulf exchanges (Tadawul above all) are the capital-raising story, recycling energy surpluses into listed national champions."
      ]
    },
    {
      "id": "stablecoins-as-market-infrastructure",
      "title": "Stablecoins as Market Infrastructure",
      "category": "Crypto",
      "date": "2026-08",
      "summary": "Stablecoins stopped being a crypto niche and became a parallel dollar-settlement system. What that means for banks, payments and data.",
      "body": [
        "Stablecoins now settle trillions of dollars a year — volumes in the range of the major card networks — with a combined supply above a quarter of a trillion dollars, almost all of it USD-denominated. Functionally, they are offshore dollar accounts with instant, programmable settlement.",
        "Two design families dominate. Reserve-backed coins (USDT, USDC) hold treasury bills and repo against issued tokens — making their issuers, in aggregate, one of the largest holders of US Treasury bills in the world. The issuers' business model is simply the interest on those reserves, which is why stablecoins became one of the most profitable business models per employee in financial history.",
        "The infrastructure consequence is underrated: a stablecoin transfer is a full settlement — no correspondent chain, no cut-off times, no T+1. For cross-border payments, emerging-market savings and machine-to-machine (AI agent) payments, stablecoins are becoming the default rail, with regulation (MiCA in the EU, the GENIUS Act in the US) converting them from grey zone to licensed instrument.",
        "For a data layer, stablecoins are also the most transparent money in existence: supply, flows and holder distribution are on-chain, queryable in real time — a preview of what all financial reference data could look like."
      ]
    },
    {
      "id": "ai-agents-and-financial-data",
      "title": "AI Agents and Financial Data Access",
      "category": "AI",
      "date": "2026-08",
      "summary": "AI agents are becoming first-class consumers of financial data. Most financial websites are unreadable to them. What machine-readable finance looks like.",
      "body": [
        "The fastest-growing consumer of financial information is no longer a person — it is an AI agent doing research, monitoring portfolios, or executing workflows on someone's behalf. These agents don't read charts or navigate menus. They need structured entities, stable identifiers, clean JSON, and honest metadata about freshness and provenance.",
        "Most financial websites fail this test completely: data lives in rendered charts, paywalled terminals, or PDFs. The sites that win agent traffic are the ones that expose the same knowledge as clean endpoints and machine-readable indexes (llms.txt, OpenAPI schemas, MCP servers) — because an agent that can consume you becomes a distribution channel, not a scraper.",
        "The emerging stack is recognizable: a knowledge layer (entities and relationships), an API layer (typed, versioned, cache-friendly), and an agent interface layer (tool definitions the agent can call directly). Provenance is not optional — an agent that cannot tell how old a number is will propagate stale data into decisions.",
        "Finance Foundation is built agent-first on purpose: every page here has an API twin, the whole dataset is one GET away, and /llms.txt tells an agent exactly what exists and how to fetch it."
      ]
    },
    {
      "id": "entity-resolution-in-finance",
      "title": "The Entity Resolution Problem",
      "category": "Data",
      "date": "2026-08",
      "summary": "Why 'which company is this?' is the hardest cheap-sounding question in finance, and how identifiers (LEI, ISIN, FIGI, MIC) actually fit together.",
      "body": [
        "Every financial dataset eventually hits the same wall: the same real-world thing appears under different names, tickers, and codes. Apple is 'Apple Inc.', AAPL on Nasdaq, ISIN US0378331005, CUSIP 037833100, LEI HWUPKR0MPOU8FGXBT394 — and AAPL means something else entirely on another venue's options feed. Merging two datasets without resolving entities silently corrupts both.",
        "The identifier landscape is layered, not redundant. LEI identifies the legal entity (who). ISIN identifies the security (what). The MIC identifies the venue (where). Tickers are venue-local shorthand — convenient and dangerously ambiguous. FIGI, an open standard, identifies an instrument at venue level and is free to use, unlike CUSIP and SEDOL, which are licensed.",
        "The expensive insight of the data industry is that relationships matter more than attributes: issuer → security → listing → venue → jurisdiction. Once those edges exist, everything else (prices, filings, news) can hang off them. This is why 'knowledge graph' is not a buzzword in finance — it is literally the shape of the domain.",
        "A practical rule for builders: store every identifier you encounter, pick one internal id you control, and treat all external ids as claims with provenance, not truth. Identifier mappings drift — companies merge, relist and rename — so resolution is a process, not a table."
      ]
    },
    {
      "id": "financial-knowledge-graphs",
      "title": "Financial Knowledge Graphs: From Tables to Relationships",
      "category": "Infrastructure",
      "date": "2026-08",
      "summary": "Why the future of financial data is a graph, not a table — and how to build one incrementally without boiling the ocean.",
      "body": [
        "Tabular data answers 'what is the value of X'. A knowledge graph answers 'how is X connected to everything else' — which is the question analysts, risk managers and AI agents actually ask. Contagion, supply chains, ownership webs, fund exposures: all of these are graph queries wearing spreadsheet costumes.",
        "A financial knowledge graph does not need exotic technology to start. It needs typed entities (company, security, market, fund, country), typed edges (lists_on, domiciled_in, managed_by, issues, regulates) and stable identifiers on the nodes. A relational database with a well-designed relationships table is a knowledge graph in the way that matters.",
        "The compounding effect is what makes graphs worth it: each new entity type multiplies the value of existing ones. Add funds to a company graph and you can suddenly answer exposure questions; add countries and you get jurisdiction risk; add banks and you get counterparty maps. The marginal dataset gets cheaper to add and more valuable, the opposite of siloed tables.",
        "For AI, the graph is the difference between retrieval and reasoning. A language model given linked entities can traverse — 'this fund is managed by this company, listed on this exchange, in this jurisdiction, whose central bank just moved rates' — instead of pattern-matching over disconnected text. Structured context is the highest-leverage input an agent can be given."
      ]
    }
  ]
}