P2P网络借贷双方交易行为研究
发布时间：2019-05-28 20:52
【摘要】：近几年来,P2P网络借贷在国内外发展迅速,凭借着快速方便的网络接口,P2P已经帮助了很多无法从传统融资渠道获得资金的需求者,也使得P2P平台的投资者获利颇丰。针对P2P网络借贷这一新兴事物,本文旨在对网络借贷平台的参与双方进行深入研究,试图从理论和实证两个方面解释和探讨借贷双方的交易行为规律。本文通过理论研究、理论模型和实证研究的方法,系统梳理和解释了网贷平台借贷双方的行为规律。在回顾与评述P2P网络借贷国内外研究成果的基础上,分别从信息不对称理论、社会资本理论和行为金融理论探讨和解释网贷平台借贷双方的行为,并构建了一个基于贝叶斯法则的P2P网贷信息瀑布模型,基于该模型,本文从理论上讨论了贷款人可能发生的羊群效应。然后,本文通过软件获取拍拍贷平台的交易数据,通过构建Logistic模型,研究了影响借款人借款标的满标的影响因素,并建立了借款满标预测模型。同时本文第四章也从实证的角度,通过后续投标影响的检验方法论证了拍拍贷平台上贷款人存在羊群效应。本文通过研究得出以下结论:在理论基础研究方面,信息不对称理论、社会资本理论和行为金融理论可以用来解释P2P网络借贷双方的行为,而信息瀑布模型也从理论上说明了贷款人发生羊群效应的可能性。在实证研究方面,聚类分析结果显示相同信用等级的借款人的行为特征却不尽相同,而不同信用等级的借款人之间也会存在某些共同的特征;对影响借款满标的因素进行分析,表明信用等级、利率等因素对借款人的借款标的能否满标有显著影响,而其中借款期限和借款次数两个因素不被建议留在模型中,故本文据此对借款人提出相关建议;而在羊群效应的实证检验中,模型结果显示当前投标数目对借款标的是否有后续投标有显著影响,因此贷款人中存在羊群效应。
[Abstract]:In recent years, P2P network lending has developed rapidly at home and abroad. With the rapid and convenient network interface, P2P has helped many demanders who can not obtain funds from traditional financing channels, and also made the investors of P2P platform quite profitable. Aiming at the new thing of P2P network lending, this paper aims to make a deep study on the participants of the network lending platform, and tries to explain and explore the transaction behavior law of the borrowers and borrowers from both theoretical and empirical aspects. Through theoretical research, theoretical model and empirical research, this paper systematically combs and explains the behavior law of both borrowers and borrowers on the online loan platform. On the basis of reviewing and reviewing the research results of P2P online lending at home and abroad, this paper discusses and explains the behavior of both borrowers and borrowers of online lending platform from the theory of information asymmetry, the theory of social capital and the theory of behavioral finance. A P2P network loan information waterfall model based on Bayes rule is constructed. based on this model, this paper theoretically discusses the herding effect of borrowers. Then, this paper obtains the transaction data of PPDAI platform through software, constructs Logistic model, studies the influencing factors that affect the borrower's loan target full standard, and establishes the loan full standard prediction model. At the same time, the fourth chapter also demonstrates the herding effect of the lender on PPDAI platform through the test method of the influence of subsequent bidding from the empirical point of view. Through the research, this paper draws the following conclusions: in the theoretical basic research, the theory of information asymmetry, the theory of social capital and the theory of behavioral finance can be used to explain the behavior of P2P network borrowers and borrowers. The information waterfall model also theoretically explains the possibility of herding effect on the lender. In the aspect of empirical research, the results of cluster analysis show that the behavior characteristics of borrowers with the same credit rating are not the same, and there will be some common characteristics among borrowers with different credit grades. This paper analyzes the factors that affect the full loan mark, and shows that the credit rating, interest rate and other factors have a significant impact on whether the borrower's loan target can be fully marked, and the two factors, the loan term and the number of loans, are not recommended to remain in the model. Therefore, this paper puts forward some relevant suggestions to the borrower. In the empirical test of herding effect, the results of the model show that the current number of bids has a significant impact on the subsequent bidding, so there is herding effect among borrowers.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F724.6;F832.4
本文编号：2487356
[Abstract]:In recent years, P2P network lending has developed rapidly at home and abroad. With the rapid and convenient network interface, P2P has helped many demanders who can not obtain funds from traditional financing channels, and also made the investors of P2P platform quite profitable. Aiming at the new thing of P2P network lending, this paper aims to make a deep study on the participants of the network lending platform, and tries to explain and explore the transaction behavior law of the borrowers and borrowers from both theoretical and empirical aspects. Through theoretical research, theoretical model and empirical research, this paper systematically combs and explains the behavior law of both borrowers and borrowers on the online loan platform. On the basis of reviewing and reviewing the research results of P2P online lending at home and abroad, this paper discusses and explains the behavior of both borrowers and borrowers of online lending platform from the theory of information asymmetry, the theory of social capital and the theory of behavioral finance. A P2P network loan information waterfall model based on Bayes rule is constructed. based on this model, this paper theoretically discusses the herding effect of borrowers. Then, this paper obtains the transaction data of PPDAI platform through software, constructs Logistic model, studies the influencing factors that affect the borrower's loan target full standard, and establishes the loan full standard prediction model. At the same time, the fourth chapter also demonstrates the herding effect of the lender on PPDAI platform through the test method of the influence of subsequent bidding from the empirical point of view. Through the research, this paper draws the following conclusions: in the theoretical basic research, the theory of information asymmetry, the theory of social capital and the theory of behavioral finance can be used to explain the behavior of P2P network borrowers and borrowers. The information waterfall model also theoretically explains the possibility of herding effect on the lender. In the aspect of empirical research, the results of cluster analysis show that the behavior characteristics of borrowers with the same credit rating are not the same, and there will be some common characteristics among borrowers with different credit grades. This paper analyzes the factors that affect the full loan mark, and shows that the credit rating, interest rate and other factors have a significant impact on whether the borrower's loan target can be fully marked, and the two factors, the loan term and the number of loans, are not recommended to remain in the model. Therefore, this paper puts forward some relevant suggestions to the borrower. In the empirical test of herding effect, the results of the model show that the current number of bids has a significant impact on the subsequent bidding, so there is herding effect among borrowers.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F724.6;F832.4
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