Stock Flows, Allocations & Fund Holdings

Connect mutual fund- and ETF-level capital movements to individual securities. Our combination of daily stock flows, stock factors, monthly holdings and allocations enable users to detect institutional demand earlier, distinguish active conviction from passive demand, quantify crowding and expected price pressure, and convert those observations into research, risk, execution or systematic-trading decisions. The data complements traditional live market, transaction and broker data providing users with a more comprehensive view.

Key Facts

• Proprietary Stock Flows dataset covering over $25 trillion assets at an aggregate level with daily (T+1), weekly (T+1) or monthly (T+26) delivery and point-in-time history back to 2010.
• Stock Allocations dataset covering over $25 trillion in assets at an aggregate level with monthly (T+26) delivery and point-in-time history back to 2010.
• Monthly Equity Holdings dataset with a 30, 60 or 90 day reporting lag covering over $30 trillion in assets at a detailed fund level delivered weekly with point-in-time history back to 2010.
• Sample daily and monthly stock factors covering flow momentum, smart money, manager positioning, crowding, conviction, rebalancing effects and multi-factor models.

Features & Benefits

• Stock flows estimate expected buying and selling pressure, investor sentiment, price pressure and fund-driven demand.
• Stock allocations show which investor groups are overweighting or reducing a stock, support peer comparison, sector/country aggregation and portfolio research.
• Equity holdings data facilitates custom factor creation, manager level attribution, crowding analysis, identifying which funds hold a security, ECM/origination targeting and independent backtesting.
• Stock factors generate long/short rankings, detect crowding and institutional conviction, capture post-index-rebalance reversals, and add signals that are often less correlated with price momentum.
• 15yrs point-in-time history enables robust backtesting.
• Data delivery over FTP or SFTP, including security master mappings, R Libraries and Quant Notebooks.

Relevant Use Cases

• Systematic equity research and alpha generation
  Combine daily estimated stock flows, monthly allocations, holdings and stock factors to rank securities and test long/short strategies.
• Portfolio construction and factor diversification
  Add momentum, conviction and ownership-dispersion signals alongside fundamental, valuation or price factors to create more diversified multi-factor models, sector- or region-neutral portfolios, and alternative signals when traditional factors underperform.
• Crowding, liquidity and risk monitoring
  Use ownership dispersion, conviction and holdings changes to identify concentrated ownership and potential forced or correlated selling.
• Trade execution and market-impact forecasting
  Use expected fund-driven flows and active/passive splits to anticipate buying or selling pressure around market open, index changes and fund subscriptions or redemptions.
• Sell-side research, ECM and origination
  Identify which funds hold a security, how their exposure has changed and which managers may be affected by an IPO, secondary offering or corporate event in order to create more targeted investor lists, differentiated research, capital-markets pitch books and portfolio-manager outreach.