This note is computed from Finance Foundation's own dataset (seed-0.4): 121 systemically significant companies worth roughly $47tn together, mapped to markets, indices, funds and jurisdictions. The covered set is curated by systemic importance, so every figure below describes this universe, not the whole world — but the set was chosen to represent where the world's market value actually sits, and the shape it reveals is hard to dismiss.
Concentration by country: the United States accounts for 45% of covered companies but 72% of covered market value. Concentration by company: the top ten names — NVIDIA, Microsoft, Apple, Alphabet, Amazon, Meta, Saudi Aramco, Broadcom, Tesla, Berkshire Hathaway — are 46% of the total. A map of 'global' equity value is, to a first approximation, a map of about ten balance sheets.
The fund layer makes the concentration self-reinforcing. Of the ten equity funds in our graph with holdings data, eight list the same seven companies — NVIDIA, Microsoft, Apple, Amazon, Meta, Alphabet, Tesla — among their top holdings; Broadcom appears in seven. An investor diversifying across an S&P 500 tracker, a world index fund and a Nasdaq fund holds the same seven stories three times. In graph terms: the 'holds' edges from different funds converge on the same handful of nodes.
The index layer shows the same convergence from another angle: 25 covered companies sit in two major indices at once (S&P 500 + Nasdaq-100 in the US, DAX + EURO STOXX 50 in Europe, Hang Seng + CSI 300 for the Chinese dual-listings). Index membership multiplies passive demand for exactly the names that are already the largest — the mechanism behind the fund-overlap pattern above.
One more finding surfaced by building the identifier layer rather than by analyzing it: even at this size, open entity resolution has holes. We could verify LEIs for 119 of 121 companies via GLEIF — but Chevron, a $300bn supermajor, and Kweichow Moutai, long China's most valuable listed company, could not be confidently resolved by legal name, country or ISIN mapping. If the connective tissue of open financial data fails on names this large, the long tail is far worse — which is, in one sentence, why this site exists.