2024/02/25 by Bruno Gašperov, Gašperov, Bruno, Marko Ðurasević +3
Computer Science · Decision Sciences · Engineering · #Computational Finance (q-fin.CP) #Constraint Satisfaction and Optimization #FOS: Economics and business #Multi-Criteria Decision Making #Portfolio Management (q-fin.PM) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2402.16118
openalex publication_date 2024/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The majority of standard approaches to financial portfolio optimization (PO) are based on the mean-variance (MV) framework. Given a risk aversion coefficient, the MV procedure yields a single portfolio that represents the optimal trade-off between risk and return. However, the resulting optimal portfolio is known to be highly sensitive to the input parameters, i.e., the estimates of the return covariance matrix and the mean return vector. It has been shown that a more robust and flexible alternative lies in determining the entire region of near-optimal portfolios. In this paper, we present a novel approach for finding a diverse set of such portfolios based on quality-diversity (QD) optimization. More specifically, we employ the CVT-MAP-Elites algorithm, which is scalable to high-dimensional settings with potentially hundreds of behavioral descriptors and/or assets. The results highlight the promising features of QD as a novel tool in PO.