In this thesis we investigate the application of two heuristic methods, genetic
algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The
model is based on the classical mean-variance approach, but enhanced with floor and
ceiling constraints, cardinality constraints and nonlinear transaction costs which
include a substantial illiquidity premium, and is then applied to a large I 00-stock
portfolio.
It is shown that genetic algorithms can optimise such portfolios effectively and within
reasonable times, without extensive tailoring or fine-tuning of the algorithm. This
approach is also flexible in not relying on any assumed or restrictive properties of the
model and can easily cope with extensive modifications such as the addition of
complex new constraints, discontinuous variables and changes in the objective
function.
The results indicate that that both floor and ceiling constraints have a substantial
negative impact on portfolio performance and their necessity should be examined
critically relative to their associated administration and monitoring costs.
Another insight is that nonlinear transaction costs which are comparable in magnitude
to forecast returns will tend to diversify portfolios; the effect of these costs on
portfolio risk is, however, ambiguous, depending on the degree of diversification
required for cost reduction. Generally, the number of assets in a portfolio invariably
increases as a result of constraints, costs and their combination.
The implementation of cardinality constraints is essential for finding the bestperforming
portfolio. The ability of the heuristic method to deal with cardinality
constraints is one of its most powerful features. / Decision Sciences / M. Sc. (Operations Research)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/16224 |
| Date | 06 1900 |
| Creators | Busetti, Franco Raoul |
| Contributors | Potgieter, P. H. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (viii, 93 leaves) |
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