Multiobjective optimization using differential evolution for real-world portfolio optimization |
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Authors: | Thiemo Krink Sandra Paterlini |
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Affiliation: | 1. Allianz Investment Management SE (AIM), K?niginstra?e 28, 80802, Munich, Germany 2. Department of Economics, CEFIN & RECent, University of Modena and Reggio E., Viale J. Berengario 51, 41100, Modena, Italy 3. Center for Quantitative Risk Analysis, Department of Statistics, Ludwig-Maximilians-Universit?t Munich, Akademiestr. 1/I, 80799, Munich, Germany
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Abstract: | Portfolio optimization is an important aspect of decision-support in investment management. Realistic portfolio optimization, in contrast to simplistic mean-variance optimization, is a challenging problem, because it requires to determine a set of optimal solutions with respect to multiple objectives, where the objective functions are often multimodal and non-smooth. Moreover, the objectives are subject to various constraints of which many are typically non-linear and discontinuous. Conventional optimization methods, such as quadratic programming, cannot cope with these realistic problem properties. A valuable alternative are stochastic search heuristics, such as simulated annealing or evolutionary algorithms. We propose a new multiobjective evolutionary algorithm for portfolio optimization, which we call DEMPO??Differential Evolution for Multiobjective Portfolio Optimization. In our experimentation, we compare DEMPO with quadratic programming and another well-known evolutionary algorithm for multiobjective optimization called NSGA-II. The main advantage of DEMPO is its ability to tackle a portfolio optimization task without simplifications, while obtaining very satisfying results in reasonable runtime. |
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