Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539924
Title: Portfolio optimisation with transaction cost
Author: Woodside-Oriakhi, Maria
ISNI:       0000 0004 0125 204X
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 2011
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Abstract:
Portfolio selection is an example of decision making under conditions of uncertainty. In the face of an unknown future, fund managers make complex financial choices based on the investors perceptions and preferences towards risk and return. Since the seminal work of Markowitz, many studies have been published using his mean-variance (MV) model as a basis. These mathematical models of investor attitudes and asset return dynamics aid in the portfolio selection process. In this thesis we extend the MV model to include the cardinality constraints which limit the number of assets held in the portfolio and bounds on the proportion of an asset held (if any is held). We present our formulation based on the Markowitz MV model for rebalancing an existing portfolio subject to both fixed and variable transaction cost (the fee associated with trading). We determine and demonstrate the differences that arise in the shape of the trading portfolio and efficient frontiers when subject to non-cardinality and cardinality constrained transaction cost models. We apply our flexible heuristic algorithms of genetic algorithm, tabu search and simulated annealing to both the cardinality constrained and transaction cost models to solve problems using data from seven real world market indices. We show that by incorporating optimization into the generation of valid portfolios leads to good quality solutions in acceptable computational time. We illustrate this on problems from literature as well as on our own larger data sets.
Supervisor: Lucas, C. ; Beasley, J. E. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.539924  DOI: Not available
Keywords: Heuristic algorithms ; Genetic algorithm ; Tabu search ; Simulated anealing ; Optimal solutions
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