Surrogate-assisted hyper-parameter search for portfolio optimisation: multi-period considerations

• Published in: Neural computing & applications Vol. 37; no. 18; pp. 11663 - 11680
• Main Authors: van Zyl, Terence L., Woolway, Matthew, Paskaramoorthy, Andrew
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2025; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Evolutionary algorithms; Genetic algorithms; Hedging; Image Processing and Computer Vision; Multiple objective analysis; Parameters; Pareto optimization; Pareto optimum; Portfolio management; Probability and Statistics in Computer Science; S.I. : 2021 India Intl. Congress on Computational Intelligence; Sampling; Searching; Special issue on 2021 India International Congress on Computational Intelligence; Utility functions; Surrogate modelling; Evolutionary algorithm; Hyper-parameter selection; Backtesting; Artificial intelligence; Portfolio optimisation; Multi-objective optimisation
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-09176-7

Portfolio management is a multi-period multi-objective optimisation problem subject to various constraints. However, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the Pareto driven surrogate (ParDen-Sur) modelling framework to efficiently perform the required hyper-parameter search. ParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in evolutionary algorithms (EAs) alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal multi-objective (MO) EAs on two datasets for both the single- and multi-period use cases. When considering hypervolume ParDen-Sur improves marginally (0.8%) over the state-of-the-art (SOTA)-NSGA-II. However, for generational distance plus and inverted generational distance plus, these improvements over the SOTA are 19.4% and 66.5%, respectively. When considering the average number of evaluations and generations to reach a 99% success rate, ParDen-Sur is shown to be 1.84× and 2.02× more effective than the SOTA. This improvement is statistically significant for the Pareto frontiers, across multiple EAs, for both datasets and use cases.