Research On The Information Mode Used By Investors

P2P lending is based on Internet technology,and borrowers and investors directly borrow money with the help of information release and matching work on the lending platform.In the context of a large number of small and micro enterprises or family human capital investment has long been subject to financing constraints,with the help of global financial innovation and China's financial reform,P2 P lending came into being.P2P lending has developed rapidly in China,but there have also been many problems such as frequent “running platforms” and large-scale defaults of borrowers.Information asymmetry is the root cause of such phenomena.Investors can only rely on the limited information provided by the borrower and the platform to decide whether to bid or not,and cannot verify the authenticity of the information.Therefore,the development of P2 P lending faces two traps: either investors irrationally ignore the information of borrowing projects and encourage the “running” platform;or investors exaggerate the lack of information quantity and quality,and excessively avoid risks,leading to the collapse of the industry.Then,with the existing level of P2 P lending in China and the existing conditions of information resources owned by investors,can the information asymmetry be solved? Can the two potential pitfalls that it contribute to cannot be avoided? Despite the flaws in the amount and quality of information available,in reality many investors do not take the above two extremes,but save the rational use of this information-the interest rate signal does not reflect the full risk of default,then take the interest rate signal Use a combination of non-interest rate signals to help make investment decisions.If the experience of using information is summarized and refined for more P2 P lending(potential)investors to learn from,and also for reference by the regulatory authorities when formulating policies,the above problems are hopeful.To this end,this paper takes the content and characteristics of investors' information usage patterns in P2 P lending in China as the research topic.Foreign P2 P platforms use interest rate signals to reflect the project's income risk level.Investors can basically judge the risk of borrowing projects by considering interest rates.However,in China's P2 P lending market,interest rates cannot fully accurately reflect the risk of borrowing projects.In the decision-making process,investors need to observe borrower information other than interest rate signals to help them judge the default risk of investment project borrowers.This paper confirms that this special “interest-non-interest rate” signal is used in parallel to judge the investment risk model in the Chinese P2 P lending market.It also analyzes which of the borrower's information variables related to risk are classified into interest rate signals and which are not.Therefore,they are “non-interest rate signals” that require investors to directly observe and respond to them.In order to complete this work,two sets of regressions were performed.The first group returned to the “full-scale investor data set” based on the M platform.The first step of regression is the return of interest rate to multiple borrower information variables.The variables such as the amount of borrowing,the term of the borrower,the location of the borrower,the location of the household registration,whether the borrowing project has been field-certified,and the use of the borrowing are significant,indicating that the information carried by these variables has been Incorporating the interest rate signal;the variables of the borrower's gender,marital status,years of education,and whether the location and the household registration are consistent are not significant,indicating that they are not included in the interest rate signal and belong to the “non-interest rate signal variable”.The second step of regression is to return the “non-interest rate signal variable” determined by the full-scale investment amount to the interest rate and the first-step regression.It is found that the investor has the marital status of the borrower,the years of education,and whether the location is consistent with the household registration.These three non-interest rate signal variables are used in parallel with the interest rate signal to support their decision making;at the same time,the investor does not care about the borrower's gender.In the second step of the return,investors favored borrowers with married,higher education,and the same location and household registration,and are willing to invest more funds,resulting in fewer investors in the full standard.The second set of regressions is based on the “full-scale time dataset” of the M platform,which is similar to the first set of regression content steps and analysis conclusions.The first step is to return the interest rate to multiple borrower information variables.It is found that the borrowing amount,the borrowing period,the borrower's age,the borrower's household registration location,and whether the borrowing project has been field-certified is significant,indicating that the information carried by these variables has been summarized.The interest rate signal;and the borrower's gender,marital status,years of schooling,whether the location and the household registration are consistent,and the use of the loan are not significant,and belong to the “non-interest rate signal variable”.The second step of regression is the return of the non-interest rate signal variable(the amount of control borrowing)determined by the interest rate and the previous regression,and finds that the investor will use the three non-interest rate signals of the borrower's marital status,years of education and borrowing purposes.Variables are used in parallel with interest rate signals to support their decisions;at the same time,investors do not care about the gender of the borrower and whether the location and the domicile are consistent.In the second step of the return,investors favored borrowers who are married,have higher education,and borrowed for low-risk projects,and are willing to invest more money in them,resulting in less time for full bids.