Swarm intelligent optimisation based stochastic programming model for dynamic asset allocation

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Title: Swarm intelligent optimisation based stochastic programming model for dynamic asset allocation
Authors: Dang, Jing
Edelman, David
Hochreiter, Ronald
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/2734
Date: Jul-2010
Abstract: Asset allocation is critical for the portfolio management process. In this paper, we solve a dynamic asset allocation problem through a multiperiod stochastic programming model. The objective is to maximise the expected utility of wealth at the end of the planning periods. To improve the optimisation result of the model, we employ swarm intelligent optimisers, the Bacterial Foraging Optimisation (BFO) algorithm and the Particle Swarm Optimisation (PSO) algorithm. A hybrid optimiser using the Bacterial Foraging Optimisation algorithm for initialisation and the Sequential Quadratic Programming (SQP) for local search is also suggested. The results are compared with the standard-alone SQP and the canonical Genetic Algorithm. The numerical results suggest the hybrid method provides better result, with improved accuracy, stability and computing speed than using BFO, PSO, GA, or SQP alone.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE Press
Copyright (published version): 2010 IEEE
Keywords: Swarm intelligenceDynamic asset allocation
Subject LCSH: Portfolio management--Computer simulation
Swarm intelligence
Stochastic programming
Quadratic programming
DOI: 10.1109/CEC.2010.5586135
Other versions: http://dx.doi.org/10.1109/CEC.2010.5586135
Language: en
Status of Item: Peer reviewed
Is part of: 2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]
Conference Details: Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
ISBN: 978-1-4244-6909-3
metadata.dc.date.available: 2011-01-20T15:11:30Z
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

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