Information Asymmetry Reduction in Online P2P Lending

Online peer-to-peer (P2P) microloan lending practice is becoming prevalent worldwide. However, information asymmetry between lenders and borrowers in this market may create adverse selection and moral hazard problems that could eventually lead to a high risk of loan default. In the proposed study, we will develop a model to evaluate how imitation and language analysis can reduce information asymmetry, and in turn improve bidding performance. Data from China’s PpDai will be collected to evaluate our research model and hypotheses. Innovative techniques — LIWC for Chinese text analysis and PROCESS for dichotomous data analysis — will also be used to derive our research findings