Research Methodology
How we correct the systemic flaws in standard institutional sovereign wealth data.
The fundamental problem with existing sovereign wealth data—provided by incumbents like SWFI or PitchBook—is the assumption that SWFs operate like giant pensions. They do not. Analyzing PIF's domestic venture strategy using the same metrics applied to GPFG's passive public equity portfolio yields analytical garbage.
1. Forensic AUM Reconciliation
We do not rely solely on press releases. For opaque funds (e.g., ADIA or GIC), we reconstruct AUM using three triangulation points:
- Central bank foreign exchange reserve transfers.
- Current account surplus/deficit models (adjusting for host-nation fiscal break-even oil prices).
- Regulatory filings (13F, Schedule 13D in the US; equivalent disclosures in the UK and EU).
2. The "Sovereign Premium" Adjustment
When SWFs deploy directly into private markets (specifically infrastructure and tech), they frequently utilize unbranded shell companies. Standard databases miss these. We trace ultimate beneficial ownership through offshore jurisdictions to accurately attribute direct deployment, correcting the under-reporting of direct co-investments common in Preqin's data.
3. Mandate-Specific Benchmarking
We categorize funds into three distinct mandate typologies before evaluating performance:
- Savings / Endowment: E.g., GPFG, ADIA. Evaluated on 20-year real return vs. global inflation.
- Strategic Development: E.g., PIF, Mubadala. Evaluated on domestic capital formation and foreign direct investment (FDI) catalyzed.
- Stabilization: E.g., CIC (partial mandate). Evaluated on liquidity buffers during macroeconomic drawdowns.
Data Integrity Note: All figures cited in our Quarterly Reports are dated to the specific quarter of analysis and cross-referenced against primary source regulatory filings. We do not use LLMs to generate or estimate financial figures.
Frequently Asked Questions
How does this impact global markets?
Given the scale of capital involved, shifts detailed here often create macroeconomic waves, affecting everything from public equities pricing to real estate yields in Tier-1 cities.
Where does this data come from?
Our analysis is derived from primary source documents, central bank filings, and forensic accounting. Refer to our Research Methodology for a complete breakdown of our attribution frameworks, and see our Competitors analysis for why standard data often fails.
What is the "Denominator Effect"?
A common constraint where falling liquid asset prices force a halt in illiquid deployments. Use our Denominator Simulator to model this interactively.