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Digital
Library of the European Council for Modelling and Simulation |
Title: |
Hybridising Local Search With
Branch-And-Bound For Constrained Portfolio Selection Problems |
Authors: |
Fang He, Rong
Qu |
Published in: |
(2016).ECMS 2016 Proceedings edited
by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and
Simulation. doi:10.7148/2016 ISBN:
978-0-9932440-2-5 30th
European Conference on Modelling and Simulation, Regensburg Germany, May 31st
– June 3rd, 2016 |
Citation
format: |
Fang He, Rong
Qu (2016). Hybridising
Local Search With Branch-And-Bound For Constrained Portfolio Selection
Problems, ECMS 2016 Proceedings edited by: Thorsten Claus, Frank Herrmann,
Michael Manitz, Oliver Rose European Council for Modeling
and Simulation. doi:10.7148/2016-0446 |
DOI: |
http://dx.doi.org/10.7148/2016-0446 |
Abstract: |
In this paper, we investigate a
constrained portfolio selection problem with cardinality constraint, minimum size
and position constraints, and non-convex transaction cost. A hybrid method
named Local Search Branch-and-Bound (LS-B&B) which
integrates local search with B&B is proposed based on the property
of the problem, i.e. cardinality constraint. To eliminate the computational burden which is mainly due to the cardinality constraint,
the corresponding set of binary variables is identified as core variables.
Variable fixing (Bixby, Fenelon et al. 2000) is applied on the core variables,
together with a local search, to generate a sequence of simplified
sub-problems. The default B&B search then solves these restricted and
simplified subproblems optimally due to their
reduced size comparing to the original one. Due to the inherent similar
structures in the sub-problems, the solution information is reused to evoke
the repairing heuristics and thus accelerate the solving procedure of the subproblems in B&B. The tight upper bound identified
at early stage of the search can discard more subproblems
to speed up the LS-B&B search to the optimal solution to the original
problem. Our study is performed on a set of portfolio selection problems with
non-convex transaction costs and a number of trading constraints based on the
extended mean-variance model. Computational experiments demonstrate the effectiveness
of the algorithm by using less computational time. |
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