1 research outputs found
Time-limited Metaheuristics for Cardinality-constrained Portfolio Optimisation
A financial portfolio contains assets that offer a return with a certain
level of risk. To maximise returns or minimise risk, the portfolio must be
optimised - the ideal combination of optimal quantities of assets must be
found. The number of possible combinations is vast. Furthermore, to make the
problem realistic, constraints can be imposed on the number of assets held in
the portfolio and the maximum proportion of the portfolio that can be allocated
to an asset. This problem is unsolvable using quadratic programming, which
means that the optimal solution cannot be calculated. A group of algorithms,
called metaheuristics, can find near-optimal solutions in a practical computing
time. These algorithms have been successfully used in constrained portfolio
optimisation. However, in past studies the computation time of metaheuristics
is not limited, which means that the results differ in both quality and
computation time, and cannot be easily compared. This study proposes a
different way of testing metaheuristics, limiting their computation time to a
certain duration, yielding results that differ only in quality. Given that in
some use cases the priority is the quality of the solution and in others the
speed, time limits of 1, 5 and 25 seconds were tested. Three metaheuristics -
simulated annealing, tabu search, and genetic algorithm - were evaluated on
five sets of historical market data with different numbers of assets. Although
the metaheuristics could not find a competitive solution in 1 second, simulated
annealing found a near-optimal solution in 5 seconds in all but one dataset.
The lowest quality solutions were obtained by genetic algorithm.Comment: 51 pages, 8 tables, 3 figure