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

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 1.84x and 2.02x more effective than the SOTA. This improvement is statistically significant for the Pareto frontiers, across multiple EAs, for both datasets and use cases.