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

Financial innovation (Heidelberg), 2024-12, Vol.10 (1), p.103-40 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2199-4730 ;DOI: 10.1186/s40854-023-00574-3

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  • Title:
    An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression
  • Author: Wang, Da ; Zhou, YingXue
  • Subjects: China ; Economic crisis ; Economics ; Economics and Finance ; Machine learning ; Macroeconomics ; Macroeconomics/Monetary Economics//Financial Economics ; Political Economy/Economic Systems ; SHAP value ; Shapley regression ; Systemic financial crisis
  • Is Part Of: Financial innovation (Heidelberg), 2024-12, Vol.10 (1), p.103-40
  • Description: 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 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 specific references for bank regulators and policymakers to develop critical measures to maintain macroeconomic and financial stability.
  • Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Language: English
  • Identifier: EISSN: 2199-4730
    DOI: 10.1186/s40854-023-00574-3
  • Source: Springer Open Access Journals
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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