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The impact of public health emergencies on small and medium-sized enterprises: Evidence from China

Author

Listed:
  • Liu, Qingfu
  • Shi, Chen
  • Tse, Yiuman
  • Zhang, Linlin

Abstract

This article proposes a machine learning-based method to quantify the impact of public health emergencies on the economic benefits and credit risks of small and medium-sized enterprises (SMEs). Using natural language processing and text analysis methods, we construct the degree of public health emergencies index using Chinese newspaper data. Based on the panel data of Chinese SMEs, we find that public health emergencies have a strong negative impact on the economic benefits of SMEs, and the impact is uneven across industries. In addition to declining economic benefits, we determine that SMEs' credit default risk is a more serious problem. Our predictive analysis of SMEs' credit default risk after COVID-19 indicates the benefits of using machine learning methods in credit risk assessment and control, providing precise assistance for SMEs.

Suggested Citation

  • Liu, Qingfu & Shi, Chen & Tse, Yiuman & Zhang, Linlin, 2023. "The impact of public health emergencies on small and medium-sized enterprises: Evidence from China," Global Finance Journal, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:glofin:v:58:y:2023:i:c:s104402832300087x
    DOI: 10.1016/j.gfj.2023.100892
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    More about this item

    Keywords

    Public health emergencies; SMEs; Machine learning;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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