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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
