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

Abstract This study proposed a cutting-edge multistep workflow and upgraded it by addressing its flaw of not considering how to determine the index system objectively. It then used the updated workflow to identify the probability of China’s systemic financial crisis and analyzed the impact of macroeconomic indicators on the crisis. The final workflow comprises four steps: selecting rational indicators modeling using supervised learning decomposing the model’s internal function and conducting the non-linear non-parametric statistical inference with advantages of objective index selection accurate prediction and high model transparency. In addition since China’s international influence 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 financial crisis as an example. Specifically 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 financial 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 effects. Furthermore Shapley regression validates the alignment of the indicators. The final workflow
Da Wang and YingXue Zhou Da Wang: Jilin University YingXue Zhou: Jilin University
China ; Machine learning ; SHAP value ; Shapley regression ; Systemic financial crisis (search for similar items in EconPapers)
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Financial Innovation 2024 vol. 10 issue 1 1-40
Time Added
2024/03/18 03:32
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