Portfolio selection is one of the important issues in financial investments. This article is concerned with portfolio selection based on collaborative neurodynamic optimization. The classic Markowitz mean–variance (MV) framework and its variant mean conditional value-at-risk (CVaR) are formulated as minimax and biobjective portfolio selection problems. Neurodynamic approaches are then applied for solving these optimization problems. For each of the problems, multiple neural networks work collaboratively to characterize the efficient frontier by means of particle swarm optimization (PSO)-based weight optimization. Experimental results with stock data from four major markets show the performance and characteristics of the collaborative neurodynamic approaches to the portfolio optimization problems.

中文翻译：

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基于协同神经动力学优化的极小极大和双目标投资组合选择

投资组合选择是金融投资中的重要问题之一。本文关注基于协同神经动力学优化的投资组合选择。经典的 Markowitz 均值方差 (MV) 框架及其变体均值条件风险价值 (CVaR) 被表述为极小极大和双目标投资组合选择问题。然后应用神经动力学方法来解决这些优化问题。对于每个问题，多个神经网络协同工作，通过基于粒子群优化 (PSO) 的权重优化来表征有效边界。来自四个主要市场的股票数据的实验结果显示了协作神经动力学方法对投资组合优化问题的性能和特征。