英文标题：
《An Evolutionary Optimization Approach to Risk Parity Portfolio Selection》
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作者：
Ronald Hochreiter
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最新提交年份：
2015
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英文摘要：
  In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this problem when long-only portfolios are considered, the optimization problem becomes non-trivial in the long-short case. To solve this problem, we propose a genetic algorithm as well as a local search heuristic. This algorithmic framework is able to compute solutions successfully. Numerical results using real-world data substantiate the practicability of the approach presented in this paper.
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中文摘要：
本文提出了一种求解风险平价投资组合选择问题的进化优化方法。当只考虑长投资组合时，存在凸优化方法来解决这个问题，但在长-短情况下，优化问题变得非常重要。为了解决这个问题，我们提出了一种遗传算法和局部搜索启发式算法。该算法框架能够成功地计算出解。实际数据的数值结果证实了本文方法的实用性。
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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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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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