Herd behavior of investors and default risks of p2p online lending

Abstract

We examine the impact of investors’ herding on the default risk of P2P online loans. More specifically, we first decompose the number of investors in each loan into two components: the component caused by public information and the component reflecting factors other than public information. Then we investigate the effect of each component on the default risk of loans, using an ordered probit analysis. We use the data on 3,720 loans that were traded through 8percent, a Korean price-posted P2P platform from February 2015 to December 2017. We find the results as follows: First, the number of investors is determined by information that are provided by the platform and macro-economic variables (hereafter public information). Second, the number of investors explained by public information decreases the default risk of the loans. However, the number of investors reflecting factors other than public information increases the default risk of the loans. These results are interpreted as an evidence supporting ‘herding’ hypothesis: Investors follow intentionally other investors’ investment decision. These results suggest that the quantity increasement and quality improvement of public information provided by a P2P platform can improve the efficiency of P2P lending market reducing herding caused by factors other than public information.