In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of nondominated neural network models for further selection by the decision maker. A new coevolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the coevolution is posed as a multicriteria problem to evolve sparse and efficacious neural architectures. The well known dominance and decomposition based multiobjective evolutionary algorithms are augmented with a nongeometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the coevolution is augmented to accommodate the data based implications of distinct market behaviors prior to and during the ongoing COVID 19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized coevolution approach. The results on the NASDAQ index in pre and peri COVID time windows convincingly demonstrate that the proposed coevolution approach can evolve a set of nondominated neural forecasting models with better generalization capabilities.

翻译：在多目标环境下，投资组合管理者的高影响力决策可受益于评估股票指数走势的替代预测模型。本研究提出了一种新方法，用于识别一组非支配神经网络模型，供决策者进一步选择。通过将特征从拓扑视角视为输入神经元，本文提出了一种新的协同演化方法，同时选择神经网络的特征与拓扑结构（统称为神经架构）。此外，该协同演化被构建为多准则问题，以演化出稀疏且高效的神经架构。经典的基于支配与分解的多目标进化算法被增强，引入非几何交叉算子，以多样化并平衡跨冲突准则的神经架构搜索。同时，协同演化被扩展以纳入新冠疫情前后不同市场行为的数据含义。通过与先进行特征选择再设计神经拓扑的传统顺序方法以及标量化协同演化方法进行详细对比评估，针对纳斯达克指数在疫情前与疫情期间时段的结果令人信服地表明，所提出的协同演化方法能够演化出一组具有更强泛化能力的非支配神经预测模型。