英文标题：
《Active extension portfolio optimization with non-convex risk measures
using metaheuristics》
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作者：
Ronald Hochreiter and Christoph Waldhauser
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最新提交年份：
2014
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英文摘要：
We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever multi-start local search heuristic, which turns out to provide stable solutions. The heuristic solutions are compared to optimization results of convex optimization solvers where applicable. Furthermore, the approach is applied to solve problems with non-convex risk measures, most notably to minimize Value-at-Risk. Numerical results using data from both the Dow Jones Industrial Average as well as the DAX 30 are shown.
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中文摘要：
我们考虑主动扩展投资组合的优化。为此，优化问题被改写为一个随机规划模型，并使用一个聪明的多起点局部搜索启发式算法进行求解，结果证明该算法能提供稳定的解。在适用的情况下，将启发式解与凸优化解算器的优化结果进行比较。此外，该方法还用于解决非凸风险度量问题，尤其是最小化风险价值。使用道琼斯工业平均指数和DAX 30数据得出的数值结果如图所示。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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