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Adedoyin, Olatunde (2008) Application of Heuristic Methods To Portfolio Optimisation: An Object-Oriented Approach. [Dissertation (University of Nottingham only)] (Unpublished)

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Abstract

The problem of portfolio selection has always been a key concern for investors. The early

work of Markowitz (1959), known as the Mean-Variance model, has been widely adopted

as the basis for solving the portfolio selection problem. In real-world scenarios, investors

would normally impose certain constraints on their portfolio solution in order to

customise it to meet their investment needs. Incorporating these constraints into the

portfolio selection problem makes the problem nonlinear which unveils the inability of the

Mean-Variance model for solving the nonlinear portfolio selection problem.

In this study, a portfolio optimisation system (POPT) is developed. POPT incorporates

three heuristic methods based on Simulated Annealing (SA), Tabu Search (TS) and

Variable Neighbourhood Search (VNS), which are applied to the optimisation of realistic

portfolios. The optimisation model used is based on the classical Mean-Variance

approach but enhanced with cardinality, proportion and pre-assignment constraints. The

model is flexible enough to accommodate any objective function without relying on any

assumed or restrictive features of the model.

In evaluating the model, several cases are considered under varying conditions such as

portfolio size, constraints and neighbourhood size. For example, the number of assets in

a portfolio invariably increases the search space. This study evaluates the model portfolio

problems containing up to 150 assets. SA, TS and VNS are applied to each case and

comparisons of the results are examined. In all cases, the ability of VNS to produce the

best objective value in its first few iterations makes it outperform SA and TS. In order of

performance, VNS is found to be the best, followed by TS and lastly SA.

Item Type:	Dissertation (University of Nottingham only)
Keywords:	Portfolio optimisation, Tabu Search, Simulated Annealing, Variable Neighbourhood Search
Depositing User:	EP, Services
Date Deposited:	29 Sep 2008
Last Modified:	30 Jan 2018 22:12
URI:	https://eprints.nottingham.ac.uk/id/eprint/22296

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