Using Multiobjective Algorithms to Solve the Discrete Mean-Variance Portfolio Selection

K. P. Anagnostopoulos; G. Mamanis

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Using Multiobjective Algorithms to Solve the Discrete Mean-Variance Portfolio Selection

机译：使用多目标算法来解决离散平均方差产品组合选择

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In this paper we tackle the standard Markowitz mean-variance model extended to include complex constraints. We formulate the problem as a bi-objective mixed integer optimization problem, i.e. maximization of return and minimization of risk. Τo find the set of Pareto-optimal portfolios, we implement two multiobjective algorithms, a population based multiobjective optimizer and a multiobjective optimizer which uses a local search evolution strategy. Finally, we evaluate the performance of the two multiobjective evolutionary algorithms on a public benchmark data set and a data set constructed using a representative emerging market’s index.

机译：在本文中，我们解决了扩展到包括复杂约束的标准Markowitz均值模型。我们将问题作为双目标混合整数优化问题，即最大化返回和风险最小化。 ▼查找帕累托最优投资组合集，我们实施了两个多目标算法，基于群种的多目标优化器和使用本地搜索演进策略的多目标优化器。最后，我们评估了两个多目标进化算法对公共基准数据集的性能和使用代表新兴市场索引构建的数据集。

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《International Journal of Economics and Finance》 |2010年第3期|共11页
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K. P. Anagnostopoulos; G. Mamanis;
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中图分类经济学;
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Using Multiobjective Algorithms to Solve the Discrete Mean-Variance Portfolio Selection

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