Exploring the (Efficient) Frontiers of Portfolio Optimization
Date
2017-07-18Author
Craven, Matthew
Graham, David
Subject
Portfolio optimization Pareto front quadratic programming evolutionary algorithms multi-objective problem
Metadata
Show full item recordAbstract
The cardinality-constrained portfolio optimization problem is NP-hard. Its Pareto front (or the Efficient Frontier - EF) is usually calculated by stochastic algorithms, including EAs. However, in certain cases the EF may be decomposed into a union of sub-EFs. In this work we propose a systematic process of excluding sub-EFs dominated by others, enabling us to calculate non-dominated sub-EFs. We then calculate whole EFs to a high degree of accuracy for small cardinalities, providing an alternative to EAs in those cases. We can use also this to provide insight into EAs on the problem.
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Publisher
ACM
Place of Publication
Berlin, Germany
Editor
Bosman PAN
Journal
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Pagination
19-20
Conference name
GECCO 2017
Start date
2017-07-15
Finish date
2017-07-19
Publisher URL
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