Journal
RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE
Volume 58, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ribaf.2021.101448
Keywords
P2P platform collapse; Convolutional neural network; Investor comment; Textual sentiment
Categories
Funding
- National Natural Science Foundation of China [71720107002]
- Natural Science Foundation of Guangdong Province [2017A030312001]
- Joint Foundation of National Science Foundation of China-Guangdong Province [U1901223]
- Fundamental Research Funds for the Central Universities [2018JDXM02]
- Financial Service Innovation and Risk Management Research Base of Guangzhou of China
Ask authors/readers for more resources
This study examines the impact of textual sentiment from investors' comments on P2P lending platforms on the probability of platform collapse, and highlights the significance of sentiment analysis and peer agreement/disagreement in predicting platform failure.
Textual sentiment affects the investment activities of investors in traditional financial markets. Peer-to-Peer (P2P) lending market, as one of the emerging and active Internet financial markets, has recently received considerable attention from academia. However, few related studies are available. This work examines the relationship between the textual sentiment derived from investors' comments on P2P platforms and probability of platform collapse. We collect comments from an authoritative Chinese third-party P2P lending consulting platform and use a weakly supervised convolutional neural network to calculate the textual sentiment of each comment. Empirical results show that the extracted textual sentiment has a significant influence on a P2P platform's collapse. Furthermore, the agreement and disagreement from other investors of each comment are pivotal in predicting a P2P platform's failure. We find that the textual sentiment of comments regarding P2P platforms from investor communities provide insights into predicting platforms' collapse in the near future.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available