首页> 外文会议>Modelling amp; Simulation, 2009. AMS '09 >Dynamically Weighted Continuous Ant Colony Optimization for Bi-Objective Portfolio Selection Using Value-at-Risk
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Dynamically Weighted Continuous Ant Colony Optimization for Bi-Objective Portfolio Selection Using Value-at-Risk

机译:基于风险价值的双目标投资组合动态加权连续蚁群优化

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An adaptation of ant colony for continuous domains (ACOR) to bi-objective optimization problems is proposed and used to solve the optimal portfolio selection problem in Markowitzpsilas risk-return framework. The utilized risk measure is value-at-risk (VaR). In adapting ACOR to bi objective optimization, a dynamically weighted aggregation of objective values by a normalized Tchebychev norm is used to obtain a set of non-dominated Pareto optimal solutions to the problem. The proposed method (DW-ACOR) is tested on a set of past return data of 12 assets on Tehran Stock Exchange (TSE). Historical simulation (HS) is utilized to obtain an estimate of the VaR. In order to compare the performance of DW-ACOR with a successful multi objective evolutionary algorithm (MOEA), NSGA-II is also used to solve the same portfolio selection problem. A comparison of the obtained results, shows that the proposed method offers high quality solutions and a wide range of risk-return trade-offs. While NSGA-II obtains a set of somewhat more widely spread solutions, the quality of the solutions obtained by DW-ACOR is higher as they are closer to the true Pareto front of the problem.
机译:提出了一种蚁群连续域(ACO R )对双目标优化问题的适应方法,并用于解决Markowitzpsilas风险收益框架下的最优投资组合选择问题。所采用的风险度量是风险价值(VaR)。在使ACO R 适应双目标优化时,通过归一化的Tchebychev范数对目标值进行动态加权聚合,可用于获得该问题的一组非支配Pareto最优解。该方法(DW-ACO R )在德黑兰证券交易所(TSE)的一组12种资产的过去收益数据上进行了测试。历史模拟(HS)用于获得VaR的估算值。为了将DW-ACO R 的性能与成功的多目标进化算法(MOEA)进行比较,NSGA-II还用于解决相同的投资组合选择问题。比较所获得的结果,表明所提出的方法提供了高质量的解决方案和广泛的风险-收益权衡。虽然NSGA-II获得了一套分布范围更广的解决方案,但DW-ACO R 获得的解决方案的质量更高,因为它们更接近问题的真实帕累托前沿。

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