Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market
| Autor: | Zhenhua Wu, Zhijie Lin, Lin Hu, Yong Tan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050208 finance
Information Systems and Management Computer Networks and Communications 05 social sciences Financial intermediary Library and Information Sciences Peer-to-peer computer.software_genre Management Information Systems Competition (economics) Microeconomics Bayesian game Information providers Distortion 0502 economics and business Economics 050211 marketing Information bias computer Information Systems |
| Zdroj: | Information Systems Research. 32:1140-1154 |
| ISSN: | 1526-5536 1047-7047 |
| DOI: | 10.1287/isre.2020.0956 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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