For Investors & Quant Researchers · Multi Sector Holdings
Multi-Sector Holdings Data for Investors & Quant Researchers
Multi Sector Holdings data for investors: 8 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
best alternative data sources for investing · satellite imagery data for hedge funds · point-in-time fundamentals database · how do quants use multi sector holdings data
API, files, or your warehouse. Daily, weekly, or hourly.
How do quants actually use multi-sector holdings data?
Three workflows cover nearly every use case in this pack. The positioning panel pairs the two 13F trackers: 13F Info supplies quarter-over-quarter comparisons and position histories per manager - 1,116 managers sit under 'A' alone in its directory - while WhaleWisdom aggregates all filers each quarter, typically several thousand institutions reporting tens of thousands of unique securities, into screener and backtester form. That combination maps into slow-money institutional-ownership factors and hedge-fund cloning screens.
Entity resolution is the second workflow. GLEIF's Level 2 relationship files expose about 485 thousand parent-child links, enough to walk any corporate hierarchy to its ultimate parent, and OpenCorporates adds officer and registry detail from over 140 registries when an entity holds no LEI.
How much backtest history do these multi-sector holdings series carry?
WhaleWisdom reaches furthest on positioning: cleaned position-level 13F data from Q1 2001 to present, with Form 4 and 13D/G coverage from roughly 2006 and mutual fund/ETF constituents since 2019. 13F Info starts at 2013, when structured XML filings began on EDGAR, and runs through the current quarter. Both derive from the same public SEC source, so differences are cleaning and interface, not truth.
Survey microdata arrives as discrete cycles - CBECS 2018, RECS 2020 after 2009 and 2015, MECS 2018 and 2022 - which cannot confirm anything at monthly frequency. Wikipedia's Berkshire return table spans 1965 to present as a static editorial artifact, useful for narrative, not for clean series.
What is the bottom line for investors and quants?
Six of the eight datasets score 8 or better against 62.8% (1,096 of 1,744) catalog-wide, so this slice screens rich - but its edge decays fastest on the 13F panel, where every reader sees the same quarterly disclosures 45 days late.
The persona-wide view of every industry slice sits at our all investors-quants resources page.
Straight answers
What are the best alternative data sources for investing in multi-sector holdings?
Mean quality across the eight qualifying records is 8.0 versus 7.81 catalog-wide.
Is there satellite imagery data for hedge funds covering multi-sector holdings?
Not in this slice - no satellite, card-panel or transaction feed qualifies. The nearest activity proxies are quarterly 13F position changes and EIA's monthly electricity series, which run through May 2026. Satellite-class vendors sit outside this industry's qualifying set entirely.
Can I build a point-in-time fundamentals database from these sources?
Only partially. 13F filings timestamp public disclosure but arrive quarterly with a 45-day delay, so they support positioning evidence rather than event studies. GLEIF serves current state only - no historical series through its API - and EIA survey cycles republish, so archive your own vintages.
How do quants use multi-sector holdings data?
Four patterns dominate: institutional-positioning factors and cloning screens from the two 13F trackers; ownership-network traversal over GLEIF's 485 thousand parent-child links; utility revenue, price and generation factors from EIA's monthly series; and corporate-structure checks in OpenCorporates.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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