Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.

In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets between 2007 and 2024, covering multiple financial crises, including the Global Financial Crisis (GFC), the European Sovereign Debt Crisis (ESD), and the COVID-19 pandemic. By training the RNN-AE with normalized stock returns, we derive correlations embedded in the model's weight matrices. To explore the network structure, we construct threshold networks based on the middle-layer weights for each year and examine key topological metrics, such as entropy, average clustering coefficient, and average shortest path length, providing new insights into the dynamic evolution of global stock market interconnections. Our method effectively captures the major financial crises. Our analysis indicates that interactions among American indices were significantly higher during the GFC in 2008 and the COVID-19 pandemic in 2020. In contrast, interactions among European indices were more prominent during the 2022 Russia-Ukraine conflict. In examining net inter-continental interactions, the influence was stronger between Europe and America during the GFC and the ESD crisis while, the influence between America and Asia was more powerful during the COVID-19 pandemic. Finally, we determine the structural entropy of the constructed networks, which effectively monitors the states of the market. Overall, our RNN-AE based network construction method provides valuable insights into market dynamic and uncovering financial crises, offering a powerful tool for investors and policymakers.