EconPapers    
Economics at your fingertips  
 

Analysis of the impact of social network financing based on deep learning and long short-term memory

Yuanjun Zhao (), Hongxin Yu (), Chunjia Han () and Brij B. Gupta ()
Additional contact information
Yuanjun Zhao: Nanjing Audit University
Hongxin Yu: Faculty of Business Economics, Shanghai Business School
Chunjia Han: University of London
Brij B. Gupta: Asia University

Information Systems and e-Business Management, 2025, vol. 23, issue 2, No 1, 277 pages

Abstract: Abstract The risk of peer to peer lending (P2P) platform is predicted based on text data on the Internet to avoid the risk of social network financing and improve the security of social network financing. First, the transaction and review text information of a third-party P2P platform are classified for the time series of emotional changes. Second, the Granger causal relation test is used to verify the correlation between the time series of emotional changes and trading volume. Finally, a long short-term memory (LSTM) forecasting model is proposed based on investors’ emotional changes to predict the trading volume of P2P platforms using emotional changes as a reference for social network financing to avoid risks. The results show that the value of Pearson correlation coefficient between the trading volume of P2P platforms and negative emotions is -0.2088, with a P value less than 1%, indicating a correlation between emotional changes and trading volume. The Pearson correlation coefficient between the predicted and actual values is 0.7995, whereas the mean square error is 0.2190 with a fitting degree of 0.6532. This shows that the LSTM forecasting model can accurately predict the trading volume of P2P platforms with good performance in comparison with other forecasting models.

Keywords: Deep learning; Long short-term memory; Social network financing; Risk forecasting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10257-023-00665-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infsem:v:23:y:2025:i:2:d:10.1007_s10257-023-00665-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/10257

DOI: 10.1007/s10257-023-00665-9

Access Statistics for this article

Information Systems and e-Business Management is currently edited by Jörg Becker and Michael J. Shaw

More articles in Information Systems and e-Business Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-08-15
Handle: RePEc:spr:infsem:v:23:y:2025:i:2:d:10.1007_s10257-023-00665-9