1. Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures
- Author
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Massimiliano Kaucic, Mojtaba Moradi, Mohmmad Mirzazadeh, Kaucic, Massimiliano, Moradi, Mojtaba, and Mirzazadeh, Mohmmad
- Subjects
Mathematical optimization ,Optimization problem ,NSGA-II ,Computer science ,Multi-objective portfolio optimization ,Semi-variance ,CVaR ,SPEA 2 ,Intermediate crossover ,Gaussian mutation ,Evolutionary algorithm ,lcsh:K4430-4675 ,Multi-objective optimization ,Management of Technology and Innovation ,lcsh:Finance ,lcsh:HG1-9999 ,ddc:650 ,0502 economics and business ,Genetic algorithm ,lcsh:Public finance ,Selection (genetic algorithm) ,040101 forestry ,050208 finance ,05 social sciences ,Sorting ,Pareto principle ,04 agricultural and veterinary sciences ,0401 agriculture, forestry, and fisheries ,Portfolio optimization ,Finance - Abstract
In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem. The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets. The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics. Moreover, they are able to approximate the Pareto front even in cases in which all the other approaches fail.
- Published
- 2019