| Economists have defined P2P lending as an emerging Internet finance innovation industry.Since its inception in China in August 2007,P2P lending has grown rapidly in just 12 years.Due to the non-compliant development of some platforms in the industry,the P2P lending platform has brought convenience to people’s investment and financing,and has also exposed problems.As a result,the P2P lending platform has experienced a shaip decline in transaction volume and a decrease in the yield.The platform transaction volume represents the platform’s capital absorption capacity and fund scale.In order to realize the healthy and steady development of the P2P lending industry,it is necessary to quantitatively study and analyze the transaction volume.Based on the relevant data of the "Net Loan Home" website,this paper establishes a model for the P2P lending volume in China based on qualitative analysis,cluster analysis,static panel model and dynamic panel model theory,and obtains the volume through dynamic and static models.The quantitative relationship with its influencing factors,in order to provide reference for the operators of China’s P2P lending platform to increase the transaction size.(1)Through qualitative analysis,it is concluded that most of China’s P2P lending platforms are concentrated in the developed coastal areas of the east,the platform background is diversified,the industry borrowing period is reasonable,the yield is gradually stable,and investors’ investment tends to be rational.The cluster analysis of transaction volume further shows that China’s P2P lending has obvious geographical discrimination,and investors prefer the eastern coastal developed cities like Guangdong,Beijing,Shanghai and Zhejiang.At the same time,the P2P lending volume in these four regions can represent the overall situation of China’s P2P lending industry.(2)Using the panel data of a total of 79 online lending platforms in the four regions of Guangdong,Beijing,Shanghai,and Zhejiang from January to December 2018,based on the static panel model theory,the volume of the P2P network lending industry in China was established.According to statistical test and economic qualitative analysis,the three static panel models are the most effective for the fixed effect variable intercept model and the fixed effect variable coefficient model.The fixed effect variable intercept model is used to obtain the P2P online loan platform transaction in China.The quantity and its influencing factors have a common change law as a whole:the order of direct impact of each influencing factor on volume is capital leverage(1.822)>number of investors(0.649)>balance to be paid(0.439)>number of borrowers(0.102)>Average expected rate of return(0.038)>borrowing unit(0.0085);the order of direct impact of each influencing factor on volume is time leverage(0.16)>operating time(0.0971).Through the fixed-effect variable coefficient model,it is concluded that the trading volume of China’s P2P lending platform and its influencing factors are inconsistent.The trading volume of each P2P lending platform and its influencing factors have their own regression models;The variable coefficient model shows that the transaction volume of the P2P lending platform in China is different from the influencing factors:the order of the direct expected rate of return to the volume is the VC(0.3136)>State-owned(0.3080)The order of direct impediment to transaction volume is the listing system(0.0787)>private sector(0.0358);the direct contribution of the balance to be paid is the listing system(0.9816)>wind investment system(0.629)>private department(0.4869)>The State-owned Assets Department(0.2648);the direct promotion of the number of investors on the volume of transactions is:State-owned Department(0.3446)>Venture Capital(0.1943)>Private Department(0.1909)>Listing Department(0.0610);The order of direct promotion of volume is listed in the Department of Listing(4.6289)>Venture Capital(4.1120)>Private Department(3.5198)>State-owned Assets(3.4853);Time Leverage vs.Volume Order direct role in promoting the listed line(8.0191)>VC line(0.0374),the order of the volume of the direct impediment to financing the Department of State(1.2429)>private line(0.6076).(3)Using the panel data of a total of 79 lending platforms in the four regions of Guangdong,Beijing,Shanghai,and Zhejiang from January to December 2018,using the dynamic panel model theory of fixed-effect variable intercept,The dynamic panel model of fixed-effect variable intercept is established in the transaction volume of China’s P2P lending industry.The estimation results of the main variables in the dynamic panel model regression are consistent with the estimation results in the individual fixed-effect variable intercept model,that is,the average expected rate of return,to be returned.The seven factors of balance,borrowing number,loan bid,investment number and capital leverage all directly contributed to the volume of transactions.Both operating time and time leverage directly hindered the trading volume.Through the analysis results of individual fixed effect variable intercept model and dynamic panel model,combined with the development status and characteristics of China’s P2P lending industry,suggestions are put forward:In order to expand the transaction scale in the future,P2P lending platform should conduct online investigation and offline verification of borrowing.On the basis of basic information of people,pay attention to the adjustment of the average expected rate of return,the balance to be repaid,the number of borrowers,the number of borrowings,the number of investors,etc.In order to improve the risk control system,attention should be paid to the regulation of capital leverage and time leverage.Improve the effective principal security system. |
