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

被引：2

作者：

Liu, Qingfu ^{[1
,2
]}

Shi, Chen ^{[3
]}

Tse, Yiuman ^{[4
]}

Zhang, Linlin ^{[5
]}

机构：

[1] Fudan Univ, Sch Econ, Shanghai, Peoples R China

[2] Yanqi Lake Beijing Inst Math Sci & Applicat, Beijing, Peoples R China

[3] Fudan Univ, Sch Data Sci, Shanghai, Peoples R China

[4] Univ Missouri St Louis, Dept Finance & Legal Studies, St Louis, MO USA

[5] Fudan Univ, Sch Social Dev & Publ Policy, Shanghai, Peoples R China

来源：

GLOBAL FINANCE JOURNAL | 2023年 / 58卷

基金：

中国国家自然科学基金;

关键词：

Public health emergencies; SMEs; Machine learning;

D O I：

10.1016/j.gfj.2023.100892

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页数：13

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