Compute, Complexity, and the Scaling Laws of Return Predictability
Machine learning return prediction, studied by Timmermann and Vulicevic in 2026 with a large language model. The abstract mentions a Sharpe ratio but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Using our measure of market efficiency, we find that a 25% increase in compute at the margin would have raised an investor's Sharpe ratio by roughly 10% over the last 30 years, implying economically meaningful rents to computational sophistication.”
StrategyMachine learning
TechniqueLarge language model
ClaimSharpe ratio
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
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