The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm

• 作者: K. Liagkouras K. Metaxiotis
• 来源期刊: 软件工程与应用(英文)
• 学科: 医学
• 关键词: MULTIOBJECTIVE OPTIMIZATION EVOLUTIONARY ALGORITHMS PORTFOLIO OPTIMIZATION
• 年，卷（期）: rjgcyyyyw_2013,（7）
• 页码范围: 22-29
• 页数: 8页
• 分类号: R73

摘要

The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the search effort where is needed more and provides a well formed efficient frontier with the solutions spread across the whole frontier. We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100.