Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

Kuang, Jiaqi; Ji, Xudong; Cheng, Peng; Kallinterakis, Vasileios Bill

Abstract

This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

Citation

Kuang, J., Ji, X., Cheng, P., & Kallinterakis, V. B. (2023). Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market. Systems, 11(3), Article 133. https://doi.org/10.3390/systems11030133

• Journal Article Type: Article
• Acceptance Date: Feb 27, 2023
• Online Publication Date: Mar 1, 2023
• Publication Date: 2023
• Deposit Date: Jun 8, 2023
• Publicly Available Date: Jun 8, 2023
• Journal: Systems
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 3
• Article Number: 133
• DOI: https://doi.org/10.3390/systems11030133
• Public URL: https://durham-repository.worktribe.com/output/1172563