Authors:
Riley Herman
and
Malek Mouhoub
Affiliation:
Department of Computer Science, University of Regina, Regina, Canada
Keyword(s):
Multi-Objective Evolutionary Computation (MOEA), Metaheuristics, Portfolio Optimization.
Abstract:
A common problem facing many is the tension between doing what aligns with our values and doing what is fiscally best. We propose a system leveraging Multi-Objective Evolutionary Computation, specifically MOEA/D, to produce highly performant portfolios tailored to an individual’s Environmental, Social, and Governance (ESG) preferences given a custom survey that we have designed. The survey is conducted to construct a weighting to normalize a given investor’s own responses and allow a single portfolio from the collection of the best portfolios to be matched to that investor. We have adopted two potential architectures to build our proposed system: Architecture 1, where the optimization is run for each investor that takes the survey, and Architecture 2 where a multi-objective optimization is run less frequently and the investor is given a portfolio from the Pareto front. This subset consists of all the non-dominated portfolios. The user may have different experiences, including quality
or response time, depending on the architecture chosen. The results of the experiments we conducted demonstrate that both architectures performed comparably and produced high-quality portfolios. However, the best portfolio from Architecture 2 was better in most respects than any portfolio from Architecture 1. All Architecture 1 portfolios were more significantly tailored to each of the individuals’ preferences. For Architecture 2, a limited number of high performing portfolios was generated: as a result, more investors would potentially be recommended to the same few portfolios, especially in comparison to Architecture 1.
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