Finding Near-Optimal Portfolios With Quality-Diversity
Applications of Evolutionary Computation Lecture Notes in Computer Science(2024)
摘要
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.
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