Heuristic Approaches To Realistic
Portfolio Optimisation
Author(s)
F. Busetti
Abstract
Previous research in portfolio optimisation has incorporated some real-world
aspects but, to the best of our knowledge, none has incorporated all of them in a
model applicable to actual real-world portfolios.
We therefore develop a realistic model, investigate the efficiency of its
solution by two heuristic methods, genetic algorithms and tabu search, and then
examine the insights provided by the optimisation of real portfolios.
Our model is based on the classical mean-variance approach, enhanced with
floor and ceiling constraints, cardinality constraints and nonlinear transaction
costs that include a substantial illiquidity premium, and is applied to a large 100-
stock portfolio.
We find that for large portfolios the performance of genetic algorithms is
three orders of magnitude better than that of tabu search.
The results confirm that both floor and ceiling constraints have a substantial
negative impact on real portfolio performance. Optimal portfolios with nonlinear
costs and cardinality constraints often contain a large number of stocks with very
low weightings. Their function is to diversify risk, and floor constraints hamper
this, damaging portfolio performance. In addition, nonlinear transaction costs
that are comparable in magnitude to forecast returns tend to diversify portfolios
materially.
Keywords: portfolio optimisation, efficient frontier, heuristic, genetic algorithm,
tabu search.
1 Introduction
While the basis for portfolio optimisation was established by Markowitz [1] in a
seminal paper over 50 years ago, it is often difficult to incorporate real-world
constraints into the classical theory. In practical portfolio construction there are
Keywords
portfolio optimisation, efficient frontier, heuristic, genetic algorithm,
tabu search.
Related Book
Other papers in this volume
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