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EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms by Alfonso Guarino; Domenico Santoro; Luca Grilli; Rocco Zaccagnino; Mario Balbi is a Computer Science article available to read on EtoBox.

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## Abstract __Optimal portfolio selection__—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically __Nondominated Sorting Genetic Algorithm__-II (NSGA-II), which tries to __maximize__ the yield and __minimize__ the risk, simultaneously. The system, named __EvoFolio__, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. __EvoFolio__ is an __interactive__ genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how __EvoFolio__ can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of

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Author

Alfonso Guarino; Domenico Santoro; Luca Grilli; Rocco Zaccagnino; Mario Balbi

Publisher

Springer Science and Business Media LLC

Published

2024

Language

EN

Field

Computer Science (Physical Sciences)