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NewsMaterial3 min readDisconfirmAI Editorial

Rob Carver: A New Approach to Trading Cost Optimization — Shrink the Sharpe Ratio First, Then Deduct Costs

In the 11th installment of his 2026 portfolio optimization series, systematic trading author Rob Carver proposes a new method for handling trading costs: shrink the estimate of the gross-return Sharpe ratio, then deduct per-trade costs from this shrunk value. This naturally reflects the fact that costs are more certain than returns, eliminating the need for an arbitrary cost multiplier. The article also discusses the 'cliff effect' caused by excluding high-cost instruments and its mitigation strategy, while presenting the current complete optimization architecture.

2026-11-04 21:32 GMT+8

Systematic trading practitioner Rob Carver has proposed a new method for handling trading costs in the 11th article of his 2026 portfolio optimization series. His starting point is a widely accepted observation in quantitative investment: trading costs are easier to predict than expected returns. Costs are relatively deterministic figures, whereas return estimates are filled with noise. So, when optimizing predicted weights, how can this asymmetry be naturally reflected in the results?

Carver lists six existing solutions, ranging from completely ignoring costs and using net returns, to multiplying costs by a factor greater than 1 to artificially amplify their weight. He argues that while the multiplier method seems intuitively correct—since costs are more certain, they should carry greater weight—it introduces an additional parameter, 'factor,' which requires further optimization and increases complexity. His proposed new solution is to first perform a shrinkage estimate on the gross-return Sharpe ratio, and then directly deduct each transaction's cost from the shrunk value.

The logic behind this method is that shrinkage itself already handles the uncertainty of return estimates. When you shrink a noisy Sharpe ratio estimate, you are essentially saying, 'I am not very confident about how good this return prediction is.' Costs do not require shrinkage because they are known. Therefore, shrinking first and then deducting naturally achieves the effect of giving more weight to certain costs and less weight to uncertain returns, without needing to manually set a multiplier. The specific parameters Carver provides are: a Sharpe ratio shrinkage of 0.5, a correlation coefficient shrinkage of 0.75, and higher shrinkage when data spans less than five years.

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However, Carver admits that this method suffers from a cliff effect: he sets a threshold of 0.01 Sharpe ratio points as the cost cap; tools exceeding this threshold are dynamically excluded by the optimizer. An instrument with a cost of 0.012 and one with a cost of 0.008 receive drastically different treatment in the optimization. His mitigation strategy is to cap costs at 0.01 at the optimization level, making high-cost instruments behave as if their cost is exactly 0.01 during weight calculations, but still excluding them at the actual execution level. He acknowledges this remains a double standard and somewhat messy, but it is better than the previous approach of completely ignoring costs.

The article concludes by presenting Carver's current 'gold standard' architecture for forecast weight optimization: from outside to inside, it consists of rolling out-of-sample optimization, cross-instrument average pooling (averaging optimizations based on three datasets for gross returns: instrument-specific, asset-class-level, and full-sample), reverse weight processing (allowing for the discovery of effective strategies the author did not anticipate), clustering (targeting six clusters), and finally, innermost shrinkage plus cost deduction. Instrument weights use the same structure but omit the pooling step. Carver explicitly states that the next article will test this architecture on a smaller, tractable portfolio.

Sources

  • qoppac.blogspot.com↗
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