Carlos Sato

Gain matrix: asserting that a bench test on 250 S&P 500 assets is sufficient for large-scale portfolio optimization presents asymmetric incentives. Who benefits from presenting such a test as significant, ignoring that market dynamics for thousands of assets are radically different for funds managing billions? If everyone bases their strategies on such limited samples, the risk of an underperforming equilibrium, where the real opportunities of multi-million dollar portfolios are ignored, becomes the dominant strategy. A pension fund manager like the GPIF of Japan, with its thousands of positions, could not ignore the inherent complexity of managing a much larger universe based on such a limited sample.

Executing sub-problems on a 64-qubit Barium development system, while interesting, does not intrinsically alter the gain matrix in the face of complexities. The incentive here is to optimize locally, but if this optimization ignores the increasing interdependencies with a larger number of assets, the expected gain collapses. Think of a trading algorithm that works perfectly in one sector but destabilizes the entire market when applied globally due to unmodeled reflexivity effects.

Where does this fail in the Swiss markets? A benchmarking conducted on only 250 S&P 500 stocks is not sufficient evidence to claim large-scale portfolio optimization.
The weak link here is that this test ignores overall diversification and the asset complexity typically found in Swiss mandates, for example.
Our portfolios include much more than U.S. stocks: we manage thousands of instruments, from trust bonds to commodities, including exotic currencies and derivative products across numerous jurisdictions.
Confirming the robustness of such a system would require, at a minimum, a simulation including liquidity risks specific to each market and local regulatory constraints.
For a Swiss private bank, a portfolio of 250 stocks is anecdotal, whereas operational reality demands dynamic optimization across tens of thousands of global positions.

Who has an interest in presenting a simple laboratory benchmark as operational proof for large-scale portfolio optimization?
The researchers' payment matrix prioritizes publication, while managers focus on risk-adjusted returns and cash flow management.
Funds in the Cayman Islands must consider asymmetric incentives and market vulnerabilities; a universe of 250 assets does not predict performance with thousands of illiquid positions.
If all managers naively adopted such a system, strategies would become predictable, leading to a suboptimal Nash equilibrium where no one gains significantly, as during the Long-Term Capital Management (LTCM) collapse where sophisticated models failed against unforeseen market dynamics.