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 non-dominated neural network models for further selection by the decision-maker. A new co-evolution 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 co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution 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 co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

在多目标环境中，投资组合经理的重大决策可以从评估股指变动的替代预测模型中受益。本研究提出了一种新方法来识别一组非支配神经网络模型，以供决策者进一步选择。提出了一种新的协同进化方法来同时选择神经网络的特征和拓扑（统称为神经架构），其中从拓扑角度将特征视为输入神经元。此外，共同进化被提出为多标准问题，以进化稀疏且有效的神经架构。众所周知的统治力和基于分解的多目标进化算法通过非几何交叉算子得到增强，以使跨冲突标准的神经架构搜索多样化和平衡。此外，协同进化得到了增强，以适应持续的 COVID-19 大流行之前和期间不同市场行为的基于数据的影响。使用传统的特征选择顺序方法和神经拓扑设计以及标量化协同进化方法进行了详细的比较评估。三个市场指数（纳斯达克、纽约证券交易所和标准普尔 500）前期和后期的结果COVID 时间窗令人信服地证明，所提出的共同进化方法可以进化出一组具有更好泛化能力的非支配神经预测模型。