Kernel Learning for Mean-Variance Trading Strategies
“We compare this with the signature-based framework of (Futter, Horvath, Wiese, 2023) and demonstrate that both significantly outperform classical Markovian methods when the asset dynamics or predictive signals exhibit temporal dependencies for both synthetic and market-data examples.”
StrategyPortfolio optimization
TechniqueFeedforward networkmathematical derivation
ClaimExcess return
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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