ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection

TL;DR

ParDen introduces a surrogate-assisted algorithm for multi-objective portfolio optimization, significantly reducing evaluation costs and improving Pareto front quality compared to existing methods.

Contribution

It presents a novel surrogate-assisted approach for portfolio selection that decreases computational costs and enhances solution quality over state-of-the-art algorithms.

Findings

ParDen reduces evaluations by nearly one-third.

ParDen outperforms existing algorithms in Pareto front quality.

Metaheuristics combined with ParDen improve portfolio optimization efficiency.

Abstract

Portfolio optimisation is a multi-objective optimisation problem (MOP), where an investor aims to optimise the conflicting criteria of maximising a portfolio's expected return whilst minimising its risk and other costs. However, selecting a portfolio is a computationally expensive problem because of the cost associated with performing multiple evaluations on test data ("backtesting") rather than solving the convex optimisation problem itself. In this research, we present ParDen, an algorithm for the inclusion of any discriminative or generative machine learning model as a surrogate to mitigate the computationally expensive backtest procedure. In addition, we compare the performance of alternative metaheuristic algorithms: NSGA-II, R-NSGA-II, NSGA-III, R-NSGA-III, U-NSGA-III, MO-CMA-ES, and COMO-CMA-ES. We measure performance using multi-objective performance indicators, including Generational Distance Plus, Inverted Generational Distance Plus and Hypervolume. We also consider meta-indicators, Success Rate and Average Executions to Success Rate, of the Hypervolume to provide more insight into the quality of solutions. Our results show that ParDen can reduce the number of evaluations required by almost a third while obtaining an improved Pareto front over the state-of-the-art for the problem of portfolio selection.