Differential Evolution for Multiobjective Portfolio Optimization

Thiemo Krink () and Sandra Paterlini

Center for Economic Research (RECent) from University of Modena and Reggio E., Dept. of Economics "Marco Biagi"

Abstract: Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA II.

Keywords: Portfolio Optimization; Multiobjective; Real-world Constraints; Value at Risk; Expected Shortfall; Differential Evolution (search for similar items in EconPapers)
JEL-codes: C61 D81 G11 (search for similar items in EconPapers)
Pages: pages 32
Date: 2008-06
New Economics Papers: this item is included in nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)

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