How the Economic Policy Uncertainty (EPU) impacts FinTech: The implication of P2P lending markets

被引：0
|
作者：
Zhou, Fuqin [1 ]
Chang, Aichih [1 ]
Shi, Jim [1 ]
机构：
[1] New Jersey Inst Technol, Martin Tuchman Sch Management, 184-198 Cent Ave, Newark, NJ 07102 USA
来源：
FINANCE RESEARCH LETTERS | 2024年 / 70卷
基金：
美国农业部;
关键词：
FinTech; Economic policy uncertainty (EPU); P2P; LendingClub; Prosper; RETURNS; RISK;
D O I：
10.1016/j.frl.2024.106268
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
This study investigates the impact of state-level Economic Policy Uncertainty (EPU) on FinTech lending marketplace. Leveraging two large-scale Peer-to-Peer (P2P) datasets of LendingClub and Prosper from 2010 to 2019 alongside the news-based EPU index, we unveil a causal relationship between the EPU and both the interest rates and loan amounts. Economically, a one-standard- deviation increase in EPU causes 4.49 and 8.90 basis points change in P2P loan rates, and 1.56% and 1.28% variation in loan amounts, for LendingClub and Prosper, respectively. Furthermore, the national-sourced EPU has a relatively stronger effect on LendingClub, while the state-sourced EPU imposes more impacts on Prosper.
引用
收藏
页数：7
相关论文
共 50 条
  • [41] Modeling investment intention in online P2P lending: an elaboration likelihood perspective
    Lin, Chieh-Peng
    Huang, Hao-Yu
    INTERNATIONAL JOURNAL OF BANK MARKETING, 2021, 39 (07) : 1134 - 1149
  • [42] Determining Factors of Peer-to-Peer (P2P) Lending Avoidance: Empirical Evidence from Indonesia
    Ali, Syaiful
    Simboh, Billy
    Rahmawati, Ulfa
    GADJAH MADA INTERNATIONAL JOURNAL OF BUSINESS, 2023, 25 (01) : 1 - 27
  • [43] Investment Recommendation with Total Capital Value Maximization in Online P2P Lending
    Tan, Yanchao
    Zheng, Xiaolin
    Zhu, Mengying
    Wang, Chaohui
    Zhu, Zhifeng
    Yu, Lifeng
    2017 IEEE 14TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE 2017), 2017, : 159 - 165
  • [44] Why do peer-to-peer (P2P) lending platforms fail? The gap between P2P lenders' preferences and the platforms’ intentions
    Galit Klein
    Zeev Shtudiner
    Moti Zwilling
    Electronic Commerce Research, 2023, 23 : 709 - 738
  • [45] Why do peer-to-peer (P2P) lending platforms fail? The gap between P2P lenders' preferences and the platforms' intentions
    Klein, Galit
    Shtudiner, Zeev
    Zwilling, Moti
    ELECTRONIC COMMERCE RESEARCH, 2023, 23 (02) : 709 - 738
  • [46] Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using Lending Club data
    Chang, An-Hsing
    Yang, Li-Kai
    Tsaih, Rua-Huan
    Lin, Shih-Kuei
    QUANTITATIVE FINANCE AND ECONOMICS, 2022, 6 (02): : 303 - 325
  • [47] Dynamic Cross-Correlations between Participants' Attentions to P2P Lending and Offline Loan in the Private Lending Market
    Zhao, Yingxiu
    Zhang, Wei
    Kong, Xiangyu
    COMPLEXITY, 2019, 2019
  • [48] Cost-sensitive Classifiers in Credit Rating A Comparative Study on P2P Lending
    Wang, Haomin
    Kou, Gang
    Peng, Yi
    2018 7TH INTERNATIONAL CONFERENCE ON COMPUTERS COMMUNICATIONS AND CONTROL (ICCCC 2018), 2018, : 210 - 213
  • [49] Lender Trust on the P2P Lending: Analysis Based on Sentiment Analysis of Comment Text
    Niu, Beibei
    Ren, Jinzheng
    Zhao, Ansa
    Li, Xiaotao
    SUSTAINABILITY, 2020, 12 (08)
  • [50] Peer to Peer (P2P) Lending Problems and Potential Solutions: A Systematic Literature Review
    Suryono, Ryan Randy
    Purwandari, Betty
    Budi, Indra
    FIFTH INFORMATION SYSTEMS INTERNATIONAL CONFERENCE, 2019, 161 : 204 - 214
12345