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An early prediction model on systemic risk under global risk: Using FinBERT and temporal fusion transformer to multimodal data fusion framework

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  • Jin, Xiao
  • Lin, Shu-Ling

Abstract

Several United States banks went bankrupt in 2023, and the total scale exceeded the subprime 2008 mortgage crisis. Thus, determining how to better predict banks’ systemic risks is crucial. While past research used quantitative data and statistical methods, rarely incorporated qualitative data, and lacked research exploring the impact of public confidence on systemic risk.

Suggested Citation

  • Jin, Xiao & Lin, Shu-Ling, 2025. "An early prediction model on systemic risk under global risk: Using FinBERT and temporal fusion transformer to multimodal data fusion framework," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:ecofin:v:76:y:2025:i:c:s1062940825000014
    DOI: 10.1016/j.najef.2025.102361
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    More about this item

    Keywords

    Systemic risk; Public confidence; BERT; Transformer; Multimodal; Natural language processing;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises

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