Stock Return Direction Forecasting: The Role of Good and Bad Information Spillover Networks

ABSTRACT This article innovatively proposes a stock return direction forecasting method by utilizing good and bad information spillover networks. First, we construct multilayer networks based on CHOL log return decomposition and Elastic‐Net‐VAR methods and analyze differences and similarities between bad and good information networks. It is found that the bad and good information networks exhibit various topology properties and evolution behaviors. Specifically, network connectedness of bad and good information layers changes over time, and during periods of financial stress, the interconnectedness between stocks increases dramatically. Besides, compared with other models, the empirical results show that the random forest (RF) prediction model outperforms other methods, and the forecasting power of the RF model is not only statistically significant but also economically valuable. Finally, a range of methods are applied to enhance the interpretability of the RF model, and we found that the core network characteristics that influence stock direction forecasting.