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- W2007677788 abstract "An anticipatory stochastic multi-objective model based on S-Metric maximization is proposed. The environment is assumed to be noisy and time-varying. This raises the question of how to incorporate anticipation in metaheuristics such that the Pareto optimal solutions can reflect the uncertainty about the subsequent environments. A principled anticipatory learning method for tracking the dynamics of the objective vectors is then proposed so that the estimated S-Metric contributions of each solution can integrate the underlying stochastic uncertainty. The proposal is assessed for minimum holding, cardinality constrained portfolio selection, using real-world stock data. Preliminary results suggest that, by taking into account the underlying uncertainty in the predictive knowledge provided by a Kalman filter, we were able to reduce the sum of squared errors prediction of the portfolios ex-post return and risk estimation in out-of-sample investment environments." @default.
- W2007677788 created "2016-06-24" @default.
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- W2007677788 date "2013-06-01" @default.
- W2007677788 modified "2023-09-30" @default.
- W2007677788 title "Anticipatory Stochastic Multi-Objective Optimization for uncertainty handling in portfolio selection" @default.
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- W2007677788 doi "https://doi.org/10.1109/cec.2013.6557566" @default.
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