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• W4297491151 abstract "We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences, and constraints beyond simple expected returns, within a flexible, forward-looking, and non-linear framework. Our empirical analysis illustrates that predicting the optimal weights directly instead of the traditional two-step approach leads to more stable portfolios with statistically better risk-adjusted performance measures." @default.
• W4297491151 created "2022-09-29" @default.
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• W4297491151 date "2022-09-28" @default.
• W4297491151 modified "2023-09-27" @default.
• W4297491151 title "Supervised portfolios" @default.
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• W4297491151 doi "https://doi.org/10.1080/14697688.2022.2122543" @default.
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