  This note focuses on the optimization of neural architectures for stock index
movement forecasting following a major market disruption or crisis. Given that
such crises may introduce a shift in market dynamics, this study aims to
investigate whether the training data from market dynamics prior to the crisis
are compatible with the data during the crisis period. To this end, two
distinct learning environments are designed to evaluate and reconcile the
effects of possibly different market dynamics. These environments differ
principally based on the role assigned to the pre-crisis data. In both
environments, a set of non-dominated architectures are identified to satisfy
the multi-criteria co-evolution problem, which simultaneously addresses the
selection issues related to features and hidden layer topology. To test the
hypothesis of pre-crisis data incompatibility, the day-ahead movement
prediction of the NASDAQ index is considered during two recent and major market
disruptions; the 2008 financial crisis and the COVID-19 pandemic. The results
of a detailed comparative evaluation convincingly support the incompatibility
hypothesis and highlight the need to select re-training windows carefully.
