Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market

Zhenhua Wu (), Lin Hu (), Zhijie Lin () and Yong Tan ()
Additional contact information
Zhenhua Wu: School of Management, Nanjing University, Nanjing, Jiangsu 210000, P.R. China
Lin Hu: College of Business and Economics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
Zhijie Lin: School of Economics and Management, Tsinghua University, Beijing 100084, P.R. China
Yong Tan: Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195

Information Systems Research, 2021, vol. 32, issue 4, 1140-1154

Abstract: Despite the popular emergence of peer-to-peer (P2P) lending platforms, relevant research investigating the role of these platforms on P2P markets still lags. In this paper, we present a model to study the market incentives of P2P lending platforms’ optimal information-reporting strategies when the following exist: (i) uncertainty on the return of loans and (ii) competition from entrants. We focus on the information bias of platforms driven by demand-side actors—investors’ optimism/pessimism about risk—while we keep the platforms being rational. We characterize platforms’ equilibrium reporting strategies under different market conditions. Surprisingly, we find that when uncertainty is significant, and the threat of entry is strong but not detrimental, the platform has incentives to bias information toward investors’ biased beliefs. This result demonstrates a case where competition and uncertainty may jointly lead to information bias. However, a properly designed uncertainty-resolution mechanism could reduce the incentive. Our findings contribute to the literature on the P2P lending market by analyzing platform decisions and offer policy implications for regulating P2P lending market.

Keywords: peer-to-peer lending platforms; information provider; financial intermediaries; information bias; perfect Bayesian equilibrium (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2020.0956 (application/pdf)

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:inm:orisre:v:32:y:2021:i:4:p:1140-1154

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().