Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review

In this paper we provide a review of the current state of research on Portfolio Management with the support of Multiobjective Evolutionary Algorithms (MOEAs). Second we present a methodological framework for conducting a comprehensive literature review on the Multiobjective Evolutionary Algorithms (MOEAs) for the Portfolio Management. Third, we use this framework to gain an understanding of the current state of the MOEAs for the Portfolio Management research field and fourth, based on the literature review, we identify areas of concern with regard to MOEAs for the Portfolio Management research field.

► Almost 80% of the authors in the field have contributed a single paper. ► US, UK and German institutions have contributed the most to the study of the field. ► The majority of the scholars (82.50%) use only two objectives in their models. ► The most popular objectives are the mean and variance derived from Markowitz’s model. ► Cardinality constraints and Lower and Upper bounds are the most popular constraints.