Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market
研究了P2P借贷平台在贷款收益不确定和面临新平台竞争时，如何选择信息报告策略，发现平台可能迎合投资者的乐观或悲观偏见，但适当的不确定性解决机制可减少这种偏差。
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.