Systemic banking crises can be very damaging to economic development, and environmental, social, and governance (ESG) can also damage national finances, but there is no research on whether ESG affects systemic banking crises, and we fill this gap. We first employ Fisher scores (FS) to select features and then use an interpretable multivariate long-short-term memory (IMV-LSTM) model with focal loss (FL) to account for class imbalance to model an early warning system (EWS) that can predict up to one year in advance. This study finds that ESG influences the occurrence of systemic banking crises, with our early warning system predicting each crisis a year in advance.

In the current situation of U.S.-China trade turbulence, this study focuses on quarterly panel data from May 2016 to September 2019 in order to verify the effectiveness of feedback trading strategy and smart money theory in stabilizing U.S.-China securities markets and to understand the role of institutional investors’ behavior, to come up with suggestions for improving and perfecting the market mechanism in stabilizing the U.S.-China securities markets. In this study, we adopt the generalized method of moments (GMM) to perform dynamic panel data analysis and discuss the changes in professional institutional investors’ behavior and equity market sentiment in the U.S. and China during the trade turbulence, and then analyze whether that behavior will suppress local stock market sentiment. Through empirical research, we found that institutional investors on both sides of the trade turbulence have a different impact on the stability of the local securities market. The behavior of institutional investors in the United States has played a role in stabilizing equity market sentiment in accordance with feedback trading strategy and smart money theory. However, the behavior of institutional investors in China is the opposite.