Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)

<Previous Article In Volume
Next Article In Volume>

Home bias in P2P Lending Platform

Authors
HaoYin Zhi1, *
1University of Shanghai for Science and Technology, Shanghai, 200093, China
*Corresponding author. Email: 1981144083@qq.com
Corresponding Author
HaoYin Zhi
Available Online 29 October 2023.
DOI
10.2991/978-94-6463-270-5_15How to use a DOI?
Keywords
Home bias; P2P; Random Forest; Lending platform; Transfer
Abstract

This article mainly concentrates on the impact of home bias in peer-to-peer (P2P) platforms. Since the absence of geographical barriers in online transfers and visible information flows to investors, the spatial barriers to trading have been broken down and it is logical that no significant local preference should be shown. However, some studies in economics and finance still suggest that home bias appears in online platforms. Therefore, the purpose of this article is to verify that home bias does influence investment preferences by using the machine learning approach of Random Forest Model [1] to analyze data collected from lending platforms. The results show that home bias does have a significant impact on investment preferences, particularly among local borrowers, where lenders often relax loan terms, making it more likely for them to lend to people from the same area. This study offers a deeper understanding of the impact of home bias, providing constructive suggestions for advancing the push methods of future P2P platforms and applications of constructed models.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
Series
Atlantis Highlights in Economics, Business and Management
Publication Date
29 October 2023
ISBN
10.2991/978-94-6463-270-5_15
ISSN
2667-1271
DOI
10.2991/978-94-6463-270-5_15How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - HaoYin Zhi
PY  - 2023
DA  - 2023/10/29
TI  - Home bias in P2P Lending Platform
BT  - Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
PB  - Atlantis Press
SP  - 131
EP  - 137
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-270-5_15
DO  - 10.2991/978-94-6463-270-5_15
ID  - Zhi2023
ER  -