2023/07/08 by Alexander Nikiporenko, Nikiporenko, Alexander
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Computational Engineering #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Portfolio Management (q-fin.PM) #Reservoir Engineering and Simulation Methods #Risk and Portfolio Optimization #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2307.04045
openalex publication_date 2023/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.