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Financial Datasets delivers structured market data for US public companies. Through Exa Connect it exposes ticker-based news: recent coverage of earnings, market reactions, and company events, keyed to a stock symbol.

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Use it for

  • Tracking news and press coverage for specific tickers.
  • Summarizing earnings reactions and market-moving events.
  • Building watchlists and monitoring portfolio companies.

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Provider ID

Use this value in dataSources: 
financial_datasets

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Example

Get recent news for NVIDIA and summarize key themes. 
from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="Get recent news about NVIDIA (NVDA) and summarize the key themes.",
    data_sources=[{"provider": "financial_datasets"}],
    output_schema={
        "type": "object",
        "required": ["ticker", "articles"],
        "properties": {
            "ticker": {"type": "string"},
            "articles": {
                "type": "array",
                "maxItems": 10,
                "items": {
                    "type": "object",
                    "required": ["title", "source", "date", "theme"],
                    "properties": {
                        "title": {"type": "string"},
                        "source": {"type": "string"},
                        "date": {"type": "string"},
                        "theme": {"type": "string", "description": "e.g. earnings, product launch, regulation"},
                    },
                },
            },
        },
    },
)
run = exa.agent.runs.poll_until_finished(run.id)

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Pairs well with

  • Particle: compare published coverage with podcast commentary.
  • Baselayer: verify the underlying entity behind a ticker.
  • Fiber.ai: enrich a public company with private-market peers and leadership contacts.