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Article
Supervised portfolios
in Quantitative Finance, 22 (12)
Voir la revue «Quantitative Finance»
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
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statistically better risk-adjusted performance measures.
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