Research On The Effectiveness Of Signal Transmission Mechanism Of Historical Information

Relative to large state-owned enterprises,small and medium-sized private enterprises have difficulty in financing from traditional financial institutions such as banks,due to their higher operating risk and lower credit guarantee.Such circumstances limit the development of small and medium-sized enterprises to a certain extent,and further inhibit their roles in improving China's economic development.In this context,P2P online lending which has advantages in guarantee mortgage and admittance threshold will play an important role in increasing the utilization efficiency of social capital and reducing the restriction of small and medium-sized private enterprises,which can form the complementary for traditional financial credit system.But there is serious information asymmetry phenomenon in P2P online lending.Therefore,the analysis on the information and information conduction effect in P2P lending will have significant practical importance in the constraints on the borrower's bad behaviors,protecting investors' rights,reducing the rates of bad debts.Through reviewing related research literature,we find that most researches focus on borrowers' current information,few researches analyze the connection between historical information and borrowing behaviors.So we make analysis on the influence of historical information on online lending behaviors under repeated lending,and construct a dynamic game model from the perspective of both the borrowers and the investors' decision-making process to put forward the research hypothesis.Next,based on the transaction data from Renrendai P2P network platform,we get several conclusions:First,the existence of borrowers' historical information or not has negative effect on the borrowing success rate and the default rate,and has positive influence on the borrowing costs,namely,the credit signal of historical information is effective.Second,historical probability has positive influence on the borrowing success rate,but it has negative influence on the borrowing costs and the default rates.Third,there is difference among the influence of various groups on online borrowing behaviors:In terms of the age,the historical financing probability's promotion effect of the success rate and the reducing effect of borrowing costs are the largest in the older borrowers,while the negative correlation between the probability of historical financing and the possibility of default is the largest among the young people.In terms of education,the positive correlation between historical financing probability and the probability of financing success rate,the negative correlation between the probability of historical financing and the borrowing cost and the possibility of default are the largest group of in the high school or below.In terms of the credit rating,the impact of historical financing probability on the lending behavior is more significant and greater in the low credit group.Fourth,the interaction between the borrowing scale and the historical information is only effective for the success rate of the borrowing,and there is no significant linear relationship between the relative borrowing scale and the borrowing cost and borrowing default rate.Finally,on the basis of the conclusions above,we put forward the following policy suggestions for borrowers,investors,P2P platform and relevant supervisors so as to alleviate the information asymmetry problem in P2P lending,reduce investment risk and promote sustainable and healthy development of P2P lending platform.The innovation of this paper is mainly focused on the following three aspects:First,the research topic has scarcity and practical significance.Few scholars studied the role of borrower's historical information in online lending behavior.In fact,historical information is the empirical results of its repayment ability,so it is very necessary to carry out research on this perspective.Second,the theoretical model of the repeated borrowing environment is relatively rare,and the model setting is slightly idealized.This paper constructs a game model with Internet loan characteristics under the condition of repeated borrowing.Third,the construction of variable indicators is more reasonable.In order to exclude the borrower from applying for the base to interfere with the results,we choose the historical financing probability instead of the historical borrowing to measure the historical information.