Knowing which ETFs hold a stock tells you where passive and thematic flows can amplify moves. A semiconductor name held heavily in sector ETFs may react to fund-level rebalancing even when its own fundamentals are unchanged. Conversely, low ETF sponsorship can mean a stock trades more on idiosyncratic news than on index-level flows.
Vantafin does not publish daily ETF creation and redemption flow figures. What we do provide is holdings data: which ETFs and funds own a given ticker, and at what weight. That context helps you interpret price action, compare peer exposure, and build sector dashboards grounded in real positions rather than estimated flow proxies.
What the endpoints return
Two REST routes cover the holdings picture. GET /etf-holdings/{ticker} returns the ETFs that hold the stock, with position sizes and weights where available. GET /institutional-ownership/{ticker} returns ETFs that hold the stock. This is not 13F manager filing data; it is ETF-level ownership useful for understanding passive sponsorship. For mutual fund exposure, GET /fund-holdings/{ticker} covers fund holders alongside the ETF view.
Each response ties back to the fund or ETF ticker so you can cross-reference AUM, category, and price action in the screener or on individual fund pages. Historical depth on holdings supports trend analysis: has SMH's weight in your coverage name risen over the past four reporting periods, or has a thematic ETF reduced exposure after a rebalance?
Query snapshots through the REST API, filter related names in the screener, or ask your assistant to pull top holders through the MCP server using get_etf_holdings. The same underlying data powers the app company pages and programmatic access, so a weight you see in the UI matches what your model ingests overnight.
How teams use it
Sector analysts compare ETF weights across peers during earnings season to see whether passive sponsorship differs between winners and laggards. A stock with rising weight in multiple semiconductor ETFs may see more correlated moves with the group even when its own guidance is company-specific. Macro desks watch broad equity ETF holdings in rate-sensitive names to gauge how duration exposure is distributed across passive vehicles.
Product and strategy teams use holdings as a sanity check on narrative. If a thematic ETF's published holdings show reduced weight in a top constituent while the stock rallies, the disconnect invites questions about reporting lag, sampling methodology, or a holder base that differs from the index weighting. None of that replaces fundamental work) but it saves you from discovering the sponsorship picture after the story is already priced in.
Add heavily held tickers to watchlists and set alerts on price or filing events for names where ETF sponsorship matters to your thesis. Alerts turn a dataset you check occasionally into a signal that meets you where you already work.
Get started
ETF and fund holdings are available through the standard REST API on paid plans. Existing API keys access the routes without migration. Browse endpoint reference in the docs, confirm rate limits on pricing, and read about our data philosophy on the about page. If you build sector dashboards, start with five names you already track and expand once you trust the reporting cadence.
Holdings data as a rotation signal
Sector rotation often shows up in ETF and fund holdings before it appears in single-stock screens. Vantafin exposes constituent weights through /etf-holdings/{ticker} and /fund-holdings/{ticker}, and /institutional-ownership/{ticker} returns which ETFs hold a given equity, not 13F manager filings, but the passive and thematic wrappers that actually move index rebalances. Together these routes answer a practical question: who owns this name through a basket, and what else sits in the same vehicle?
Pull top holdings for a sector ETF and compare weight changes quarter over quarter. A semiconductor fund adding exposure to a mid-cap name while trimming a mega-cap constituent tells a different story than either stock's standalone chart. Cross-reference holdings with company profiles on the largest weights so concentration risk is visible before a rebalance headline hits.
The MCP server wraps the same data in get_etf_holdings, which is often faster for ad-hoc desk questions than wiring a one-off REST client. Ask which ETFs hold a ticker, then drill into the top constituents of the most relevant fund. Numbers from MCP reconcile with REST because both read the same normalized pipeline.
Screening and watchlist workflows
Add holdings context as a column in your screener workflow: export finalists, then pull /etf-holdings on the sector ETFs that matter to your book. Rank names by how many large thematic funds include them above a weight threshold. That filter surfaces stocks with passive sponsorship you might miss when screening on valuation alone.
Schedule a weekly job that ranks sector ETF top holdings by weight change, then push results alongside material 8-K events on overlapping symbols. The combination catches both basket-driven flows and company-specific catalysts in one morning digest. Save high-overlap names to watchlists and enable email alerts when filing or news triggers fire on those tickers.
Overlay sector holdings shifts with the Treasury yield curve dashboard when you are testing whether rate moves drove rotation into duration-sensitive sectors. Rising yields with bond-heavy fund trims and growth equity additions is a familiar pattern; automating that cross-check saves analysts from rebuilding the same chart every Monday.
Data discipline and entitlements
Holdings data complements but does not replace single-name fundamentals. A sector ETF trimming a name while that company reports strong guidance is a tension worth investigating, not a signal to ignore either side. Pair holdings screens with management guidance tracker revisions when you hold concentrated positions in ETF-heavy sectors.
Confirm burst limits on pricing before wiring holdings pulls into a real-time loop. Most teams treat constituent data as a daily or weekly input rather than a sub-second trigger. Document your as-of assumptions in runbooks so new engineers understand how lag between reporting dates and index rebalance effective dates affects interpretation.