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The encyclopedia · Finance & Accounting · Financial decision · 1999

GMO built $8B of portfolios with mixed-integer programming, 40–60% fewer names

GMO used mixed-integer programming to build portfolios matching target exposures with 40–60% fewer stocks, cutting trading costs at least $4M a year across $8B.

Grantham, Mayo, Van Otterloo and Company (GMO)

the move

Grantham, Mayo, Van Otterloo (GMO) manages quantitative portfolios that must track the exposures their models specify. The naive implementation — replicating every position — is expensive to trade and hard to run.

GMO used mixed-integer programming to construct portfolios close to the targets in sector and security exposure, with matching liquidity, turnover and expected return, while using fewer distinct stocks and fewer transactions. The MIP approach also handled portfolios built from several subportfolios.

Across 11 quantitatively managed portfolios representing over $8 billion, the approach kept existing client business, enabled new growth, cut the number of stock names by an average of 40–60%, and reduced annual trading costs by at least $4 million — fewer tickets, lower friction, same exposure.

why it works

  • Fewer names cut trading tickets and their costs directly.
  • Matching exposure exactly kept client mandates intact.
  • Liquidity and turnover constraints stopped the model chasing cheap trades.
  • A $4M+ annual saving compounded across $8B of assets.
the payoffFind the fewest stocks that still hit the targetclever

what transfers

When the goal is a portfolio that behaves like a target, optimize for the smallest set that matches it: fewer names and fewer trades cut cost without changing exposure.

what came after

GMO kept using the MIP approach on its quantitatively managed portfolios, and the Interfaces paper became a reference for using integer programming in portfolio construction — keeping clients, cutting friction, and turning an implementation problem into a cost advantage.

references

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