Financial crisis prediction based on multilayer supervised network analysis - Qiu, Lu; Su, Rongpei; Wang, Zhouwei (Frontiers in Physics, 2022)
Financial crisis prediction is essential in preventing financial problems as its monitoring indicators help regulators judge the probability of future crises. In this context, the activities of the scientific community have been focused on the dynamics of single/multiple sequences and utilized unsupervised/supervised methods for financial crisis prediction. It is noteworthy that the cross-correlation between the risks of multiple economic entities makes financial network analysis paramount in crisis prediction. Focusing on this point, we propose a multilayer supervised network analysis(MSNA) method to train the multilayer network, and select the most suitable layer for financial crisis prediction. Specifically, we use 37 crucial stock market indices from 4 continents to create successive multilayer financial networks with 120-day windows and 1-day step by Pearson cross-correlation(PCC), variance decompositions(VD), transfer entropy(TE),minimum spanning tree(MST), directed MST(DMST), planar maximally filtered graph(PMFG) and directed PMFG(DPMFG) methods. Based on the multilayer network, we embed the graph neural network classification(GNNC) model and train the dynamic multilayer networks at each window scale(240,120 and 60 days). Finally, we conclude that the accuracy of the short window(60 days) is significantly higher than that of the long window. The network constructed by PCC with MST is the most suitable for short sequence (60 days) crisis prediction(AUC=0.959), and the network constructed by TE with DMST is the most suitable for long sequence (240 days) crisis prediction(AUC=0.772).