  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.
