Name
Title | ||
|---|---|---|
Anticipatory Stochastic Multi-Objective Optimization for uncertainty handling in portfolio selection |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CEC.2013.6557566 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
Kalman filters,Pareto optimisation,investment,risk management,stochastic programming,stock markets,uncertainty handling,Kalman filter,Pareto optimal solutions,S-Metric maximization,anticipatory stochastic multiobjective optimization model,cardinality constrained portfolio selection,metaheuristics,minimum holding portfolio selection,objective vector dynamics tracking,out-of-sample investment environments,portfolios expost return,predictive knowledge,principled anticipatory learning method,real-world stock data,risk estimation,stochastic uncertainty,sum of squared error prediction,uncertainty handling,Anticipatory learning,Kalman filter,dynamic environments,indicator-based search,portfolio selection,stochastic multi-objective optimization | Mathematical optimization,Computer science,Cardinality,Kalman filter,Multi-objective optimization,Portfolio,Risk management,Artificial intelligence,Stochastic programming,Maximization,Machine learning,Metaheuristic | Conference |
ISBN | Citations | PageRank |
978-1-4799-0452-5 | 1 | 0.36 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos R. B. Azevedo | 1 | 27 | 4.49 |
| Von Zuben, F.J. | 2 | 114 | 12.19 |