An innovative machine learning workflow to research China’s systemic fnancial crisis with SHAP value and Shapley regression

This study proposed a cutting-edge, multistep workfow and upgraded it by addressing its faw of not considering how to determine the index system objectively. It then used the updated workfow to identify the probability of China's systemic fnancial crisis and analyzed the impact of macroeconomic indicators on the crisis. The fnal workfow comprises four steps: selecting rational indicators, modeling using supervised learning, decomposing the model's internal function, and conducting the non-linear, nonparametric statistical inference, with advantages of objective index selection, accurate prediction, and high model transparency. In addition, since China's international infuence is progressively increasing, and the report of the 19th National Congress of the Communist Party of China has demonstrated that China is facing severe risk control challenges and stressed that the government should ensure that no systemic risks would emerge, this study selected China's systemic fnancial crisis as an example. Specifcally, one global trade factor and 11 country-level macroeconomic indicators were selected to conduct the machine learning models. The prediction models captured six risk-rising periods in China's fnancial system from 1990 to 2020, which is consistent with reality. The interpretation techniques show the non-linearities of risk drivers, expressed as threshold and interval efects. Furthermore, Shapley regression validates the alignment of the indicators. The fnal workfow is suitable for categorical and regression analyses in several areas. These methods can also be used independently or in combination, depending on the research requirements. Researchers can switch to other suitable shallow machine learning models or deep neural networks for modeling. The results regarding crises could provide specifc references for bank regulators and policymakers to develop critical measures to maintain macroeconomic and fnancial stability.