A multi-criteria approach to evolve sparse neural architectures for stock market forecasting

The development of machine learning based models to predict the movement of a financial market has been a challenging problem due to the low signal-to-noise ratio under the effect of an efficient market. Although, researchers have developed neural network based predictive models to address this issue, the selection of an appropriate neural architecture is seldom addressed. This study, therefore, proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs. The proposed approach formulates the neural architecture search as a multi-criteria optimization problem to balance the efficacy with the complexity of architectures. In addition, the implications of different underlying trading tendencies, which may be present in the pre-COVID and peri-COVID time periods, are investigated. An $$\epsilon $$ ϵ -constraint framework is proposed as a remedy to extract remaining concordant information from the possibly partially conflicting pre-COVID data. Further, a new search paradigm, two-dimensional swarms (2DS) is proposed for the multi-criteria neural architecture search, which explicitly integrates sparsity as an additional search dimension in particle swarms. A detailed comparative evaluation of the proposed approach is carried out by considering genetic algorithm and several combinations of empirical neural design rules with a filter-based feature selection method (mRMR) as baseline approaches. The results of this study convincingly demonstrate that the proposed approach can evolve comparatively efficient and parsimonious networks.