Abstract:
Based on the database of Fintech research center, National Institute of Financial Research, Tsinghua University and other statistical P2P data, this paper focuses on investor's ability to identify risky platforms and normal platforms according to the degree of information disclosure. Constructing the logit model and the Cox proportional risk regression model to study the Ezubao event in December 2015 and the “explosion” phenomenon after the record postponement in June 2018,this paper examines the relationship between information disclosure, risk and trading volume of platforms. It is found that information disclosure is an important factor affecting platforms' risk. The higher degree of platform information disclosure, the longer the operation time is, and the lower the possibility of risk. In addition, the higher the degree of information disclosure, the stronger the ability to resist risks. During the period of risk contagion, the higher the degree of information disclosure, the higher the platform turnover is, and less influenced by the market's negative emotions. These two points indicate that investors attach importance to information disclosed by platforms and have certain ability to identify information. Finally, it is also found that there are significant differences between the risky platforms that appear in the two risky events and those that are still in normal operation, indicating that it is a normal phenomenon of market clearance. There is no evidence that a large number of normal platforms have been dragged into risky platforms.
李苍舒, 沈艳. 风险传染的信息识别——基于网络借贷市场的实证[J]. 金融研究, 2018, 461(11): 98-118.
LI Cangshu, SHEN Yan. Information Identification of Risk Contagion: An Empirical Study Based on P2P Lending Market. Journal of Financial Research, 2018, 461(11): 98-118.
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