P2P 网络借贷文献综述及外文文献互联网金融

本份文档包含：关于该选题的外文文献、文献综述

一、外文文献

标题: Emergence of Financial Intermediaries in Electronic Markets: The Case of Online P2P Lending

作者: Berger, Sven C; Gleisner, Fabian

期刊: Business Research；卷: 2；期: 1；页: 39-65；出版年份: 2009 Emergence of Financial Intermediaries in Electronic Markets: The Case of Online P2P

Lending

Abstract

We analyze the role of intermediaries in electronic markets using detailed data of more than 14,000 originated loans on an electronic P2P (peer-to-peer) lending platform. In such an electronic credit market, lenders bid to supply a private loan. Screening of potential borrowers and the monitoring of loan repayment can be delegated to designated group leaders. We find that these market participants act as financial intermediaries and significantly improve borrowers' credit conditions by reducing information asymmetries, predominantly for borrowers with less attractive risk characteristics. Our findings may be surprising given the replacement of a bank by an electronic marketplace.

Keywords: Asymmetric information, intermediation, social lending, electronic markets

1 Introduction

The evolution of information technology in recent years has led to the development of electronic marketplaces where traditional intermediaries may be less important or even redundant for the economic interaction of market participants (Benjamin and Wigand 1995, Evans and Wurster 1997, Malone, Yates, and Benjamin 1987). Within the financial services industry, the debate about disintermediation and the future relevance of financial intermediaries (Allen and Santomero 2001, Nellis, McCaffery, and Hutchinson 2000, Schmidt, Hackethal, and Tyrell 1999) is fueled by the increasing role of electronic lending markets (P2P Lending or Social Lending) where an electronic marketplace replaces a bank as the traditional intermediary and enables the brokerage of consumer loans directly between borrowers and lenders (Hulme and Wright 2006, Meyer 2007). A recent study predicts that within the next few years such social banking platforms may have a market share of ten percent of the worldwide market for retail lending and financial planning (Gartner Inc. 2008). For the US, the P2P lending market is estimated to grow to a volume of up to ten billion USD within the next 10 years (Bruene 2007). We examine more than 14,000 credit transactions on the American electronic P2P lending platform https://www.wendangku.net/doc/db7081318.html,, covering all transactions that took place in the market between 2005-11 and 2007-09. Our analysis of the P2P credit market starts with the observation that, despite the direct mediation of loans in the marketplace, new types of intermediaries emerge as market participants provide

paid intermediary services. In the paper we then focus on the following questions: (1) From a theoretical point of view, how can these intermediaries create value in the interaction between borrowers and lenders? (2) Should all borrowers make use of an intermediary, and can it make sense to pay for intermediary services? (3) From the borrowers' perspective, what is the economic impact of intermediation in the electronic lending marketplace? Our empirical analysis is confirmatory in nature. It is based on the literature on financial intermediation (e. g., Diamond 1984, Leland and Pyle 1976) from which we derive hypotheses on the role of intermediaries in electronic marketplaces.

2 Intermediaries in electronic credit marketplaces

2.1 Electronic marketplaces and disintermediation

Markets are essential for economic activity in mediating the demand for and supply of goods and services. Intermediaries help to facilitate transactions between buyers and sellers by (1) providing transaction processing capabilities, (2) bringing enhanced levels of knowledge and expertise, and (3) adding to the transactability of a given good or service (Chircu and Kauffman 2000).

The internet has made e-commerce possible where electronic markets are becoming more important in coordinating supply and demand (Grieger 2003, Segev, Gebauer, and Farber 1999). Electronic markets can facilitate economic activity even under complex and insecure conditions (Cordella 2006), significantly reduce information and transaction costs, and may in this way displace traditional intermediaries (Malone, Yates, and Benjamin 1987). Many authors argue that once electronic markets emerge, traditional intermediaries may be threatened by an electronic brokerage effect also called disintermediation (for a literature overview see Chircu and Kauffman 2000). In sharp contrast to that, the theoretical contributions on electronic markets and disintermediation have not yet been supported by convincing empirical evidence (Chircu and Kauffman 2000, Sen and King 2003). Moreover, the displacement of traditional intermediaries may never occur. Authors like Sarkar, Butler, and Steinfield (1998) or Hagel and Singer (1999) argue that electronic markets may lead to new forms of intermediation.

2.2 Electronic lending platforms

Electronic lending platforms are electronic markets that mediate between borrowers and lenders of loans.Wefocus here on consumer loans between individual borrowers and lenders and exclude platforms for bonds or syndicated loans (Steelmann 2006). The electronic credit marketplace as a website in the World Wide Web constitutes the general conditions for peer-to-peer lending and provides the administration of current loans. Electronic lending platforms differ in the way loans are originated: Some providers mediate between borrowers and lenders themselves, whereas other providers match borrowers' credit listings and lenders' bids with an auction mechanism (Meyer 2007).

The lion share of participants in the marketplace are private individuals, although there are institutional lenders investing in some, too. There are numerous providers that operate nationally due to differing regulatory frameworks. Table 1 provides an overview of the three major Anglo-American and German providers and their

business models. A recent development of the business model of P2P lending marketplaces is that lenders may trade loans prior to maturity, increasing the liquidity of P2P loans. As of February 2009, Prosper is still in the process, whereas Lending Club has already successfully registered with the SEC to create a secondary loan market. Despite differing business models, there is one distinctive feature that these marketplaces have in common: Transactions in electronic credit marketplaces occur anonymously between fictitious "screen names". Therefore, information is asymmetrically distributed between borrowers and lenders. Loans are not collateralized and lenders face the inherent risk of default (Steelmann 2006). Despite anonymous interactions, loan listings contain additional information on potential borrowers. Lenders can evaluate individual creditworthiness through quantitative as well as qualitative figures. https://www.wendangku.net/doc/db7081318.html,, America's largest peer-topeer lending marketplace, provides an individual rating and an indicator of indebtedness in cooperation with the credit reporting agency Experian as the two main quantitative figures. The informational value of these figures should be considered high, although the degree to which consumer credit reports are accurate, complete or consistent is in dispute (Avery, Calem, Canner, and Bostic 2003). Most platforms give market participants the opportunity to provide additional personal information about their background, their financial standing and the purpose of the loan. This qualitative, "soft" information is mandatory and its validity is a priori not controlled. Borrowers thereby might have an incentive to overemphasize their "quality" (the present value of the prospective projects, their financial standing or payment behavior) in their personal descriptions (moral hazard).

2.3 Groups on https://www.wendangku.net/doc/db7081318.html,

In addition to personal profiles, borrowers and lenders can form groups. These smaller communities within the marketplace review and assess the creditworthiness of individual members. Groups are potentially beneficial for market participants by providing and verifying information or obtaining additional information about borrowers that is not publicly available. Groups lack distinct ownership and governance features as they typically exist in credit cooperatives (Davis 2001, Taylor 1971). There is no ownership of the groups, and there is no collective decision mechanism on accepting group members or granting loans. Furthermore, groups do not exclusively deal with their members. At any time, lenders from outside the group may invest in a group member's loan listing. This implies that there is no rotation of money within the group, and no specific allocation process. There are two papers that specifically examine the role of groups on the P2P lending website https://www.wendangku.net/doc/db7081318.html,: Freedman and Jin (2008) use transaction data from 200601-06 until 2008-07-31 covering around 290,000 loan listings and 25,000 funded loans. They find evidence for the idea that the monitoring within social networks provides a stronger incentive to pay off loans ex-post: Loans with friend endorsements and friend bids have fewer missed payments and yield significantly higher rates of return than other loans. Everett (2008) looks at the influence of group membership on loan default within 13,486 Prosper loans. The dataset covers funded loans from 2006-05-31 until 2007-11-06 and incorporates ex post loan performance information until 2008-05-07.

He finds that membership in a group significantly decreases loan default risk if the group enforces real-life personal connections like, e.g., employees of the same company or alumni of a certain university. Both studies presented above look at social networks in the credit market but do not specifically take the group leader into account. It is, however, not the group as an institution per se, but the group leader who decides about membership and plays a substantial role in the lending process.

2.4 Group leaders as financial intermediaries

In order to reduce information asymmetries, lenders must screen potential borrowers. Given the large number of available credit listings, it can be costly or impossible to process the information available about potential borrowers. Therefore, intermediaries emerge in the electronic marketplace offering intermediary services in order to assess and limit credit risk. Every participant in the online lending platform can found a group and become a group leader. Group leaders set membership criteria and administer the group. Groups are smaller communities within the marketplace where group members may share a bond based on employment, geography, education, common leisure activities, or other factors. The principle that people from close communities act more responsibly towards each other aims to lower the risk of defaults and therefore enables lending at better rates. Among the most important tasks of the group leader is the screening of borrowers within the group (a voluntary due diligence known as "vetting"). Within groups, it is common that borrowers send personal documents regarding their identity, income, and other pertinent information to the group leader. The group leader may also establish personal contact with the borrowers' employer to verify the personal income in order to recommend a borrower's credit listing.

All in all, the intermediary reduces uncertainty for lenders, which should be reflected in lower required risk premiums. The arguments provided above lead us to the first fundamental hypothesis:

Hypothesis H1: Borrowers within groups are able to borrow at lower credit spreads. Next, we formulate three hypotheses that enable us to decompose the role of the group leader in the lending marketplace. With imperfect information about borrowers' credit quality, lenders can use publicly observable signals to assess credit risk (Riley 1975, Rothschild and Stiglitz 1976, Spence 1973). Observable characteristics or actions can serve as signals. On the electronic lending platform Prosper, the recommendation of a credit listing by a group leader is a strong observable signal of credit quality. Borrowers can voluntarily provide additional private information regarding their financial standing to their group leader. Group leaders can then recommend credit listings within their groups. This observable recommendation serves as a signal of good credit quality for the marketplace.This leads to:

Hypothesis H2: The recommendation of a credit listing by the group leader leads to lower credit spreads.

The reliability of information produced by an intermediary is a prevalent problem in the intermediation literature. Group leaders might recommend credit listings within their group without prior diligent screening. It may be difficult or impossible for potential lenders to distinguish good information from bad. Group leaders can signal

credibility of a recommendation by bidding on the recommended credit listing. The potential investment of the group leader is an observable signal for information quality (Leland and Pyle 1976). We derive:

Hypothesis H3a: A group leader's bidding serves as a credible signal for the quality of the credit listing and results in lower credit spreads.

Hypothesis H3b: A group leader's bidding on a credit listing signals information quality and has a stronger impact on credit spreads than a recommendation by the group leader.

We derive two additional hypotheses about the reputation and the size of groups. Past activities within a group, especially regarding the diligent assessment of individual borrowers by the group leader, are only imperfectly observable. In contrast, the reputation of a group in the electronic marketplace is observable from its group rating. The group rating is a measurement of a group's performance in paying back its loans in comparison with expected (historical) default rates. A defaulted loan worsens a group's rating and therefore its reputation. Tirole (1996) shows analytically how a group's good reputation positively influences individual behavior. The group rating reflects a group's ability to assess borrowers' credit quality, and serves as a proxy for the group leader's behavior in the future.

In addition to a group leader's general ability, we argue that group reputation serves as an effective mechanism to prevent collusion between the group leader and a borrower within the group. The phenomenon of collusion (see, e.g., Tirole 1991) could be a major concern for participants in the electronic lending marketplace. This would be the case if a potential borrower could "bribe" the group leader in order to receive a recommendation and a bid. With an increasing probability for such collusive behavior, the credibility of the observable actions of the intermediary would be significantly reduced. This would be reflected in a decreasing group rating due to higher than expected defaults within the group. We deduce:

Hypothesis H4: A higher group rating leads to lower credit spreads.

When deciding to join a group, market participants face the choice of group size. At first sight, a smaller group seems to offer a potentially closeknit community in the marketplace that facilitates the interaction and closer collaboration of group members with the group leader. This is fairly comparable to the stream of literature on relationship lending that emphasizes the exchange and evaluation of "soft information" within small banks (Petersen and Rajan 1994, Elyasiani and Goldberg 2004, Berger, Miller, Petersen, Rajan, and Stein 2005). On closer examination, and presumably more important, borrowers and lenders might prefer larger groups because they generate more opportunities for exchange, collectively provide more funds for loans, and, thus, offer easier access to credit. From an investor's perspective, in addition to a larger network, bigger groups are attractive because they may enable effective "peer-monitoring" which lowers credit risk. The concept of peermonitoring, where group members have better information and intra-group monitoring leads to greater rates of repayment, is formalized in a large body of academic literature dealing with the optimal design of group lending agreements in the context of developing economies (Stiglitz 1990, Varian 1990, Chowdhury 2005, Besley and Coate 1995).

Armendariz de Aghion (1999) provides tentative arguments in favor of a positive correlation between group size and the peer-monitoring effort. Within groups in the electronic lending marketplace, even though there is no joint liability as it typically exists in group lending (see, e.g., Prescott 1997), there are several incentive mechanisms in place to create peer pressure and induce peer-monitoring. First of all, groups comprise of individuals that share a common background or interest, based on, e.g., employment, geography, education, or leisure activities. The group can be viewed as a community where anonymity is reduced and relationships are established. In those relationships lies the potential for social sanctions (see Besley and Coate 1995 for a succinct introduction), resulting in peer pressure. Moreover, in cases of loan default, limited repayments ("community payments") may be arranged. This is, to a certain extent, comparable to a joint liability. Hence, we argue that, beyond the group leader's functions, there are peer-monitoring effects increasing with group size. These effects are perceived and valued by lenders, resulting in a lower demanded risk premium.

This leads to:

Hypothesis H5: Increasing group size leads to lower credit spreads.

4 Empirical Study

4.1 Methodology

We apply OLS regression analysis in order to determine the factors that impact the credit spreads as the outcome of the credit transactions in the marketplace and test the hypotheses H1 through H5. As a robustness test to control for a potential estimation bias due to self-selection in the choice of an intermediary, we further apply the matching method explained in section 4.5. Econometric matching techniques were developed by Rosenbaum and Rubin (1983) and extended by Heckman and Robb Jr (1985). The methods take into account the fact that the characteristics of group members may differ significantly from those of non-group members and ensure that such observed characteristics are not biasing the regression estimations.

4.2 Dependent and Independent Variables

In our empirical analysis of intermediaries on an electronic P2P lending platform, the interest rates measure how successfully borrowers can access capital. Lower interest rates indicate better access to capital. The interest rates on Prosper should generally be interpreted with respect to market interest rates (de Bondt 2005). As the dependent variable we therefore analyze the spread over three-year interest rate swaps (on the use of swap rates as a proxy for the risk-free rate see Zhu 2006), measured in basis points (i.e. one hundredth of one percent) in order to control for differing market interest rates in our data. This ensures matching maturities since all loans on Prosper have a maturity of 36 months. We employ Borrower Rate, representing borrowers' total loan cost, and Borrower Rate Net which excludes a potential group fee in order to evaluate the net effect of intermediation. Daily time series of these swap rates were obtained from the website of the Federal Reserve. To facilitate the testing of our hypotheses from Chapter 3, we employ listingand group-specific variables. We present an overview of our variables in Table 2.

In order to test hypotheses H1, H2, H3a and H3b we rely on the group-related

variables Group Affiliation, Certification, Group Leader Bid, and Mandatory Review. They measure the effect of group membership, a group leader's screening of potential borrowers, and the group leader's bid for a recommended loan listing. To be able to assess the usage of a paid intermediary, we look at Paid Group as well as Group Fee. In order to evaluate the effect of a group's reputation in hypothesis H4, we use the independent variable Group Rating. Group Size measures the potential effect hypothesized in hypothesis H5.

We incorporate a number of borrower and transaction characteristics into our analyses. First we look at four criteria based on individuals' credit reports commonly used by by traditional lending institutions (Avery, Bostic, Calem, and Canner 1996). As a proxy for probability of default (PD) and loss given default (LGD), we use Credit Grade and Debt-toIncome (DTI) Grade. Both variables are provided by Prosper in cooperation with the credit reporting agency Experian. Credit grades are derived from the individual credit score, where 40 points on the credit grade scale represent one rating notch. Therefore, Credit Grade can be included as a metric variable in our regression models. Amount and Homeownership serve as additional risk characteristics.

We include two important transaction characteristics related to internet-based e-commerce: As self-disclosure may reduce uncertainty in electronic marketplaces (Tidwell and Walther 2002), we control for Visual Self-Disclosure in borrowers' loan listings with the provision of personal photographs. We include Auction as a control variable reflecting use of the auction mechanism on Prosper as this may significantly influence price determination (Klemperer 2004). Borrowers can choose the auction mechanism if they want to give lenders the chance to bid down the interest rate. Not using the auction mechanism will close a loan listing as soon as the requested loan amount is met by bidders. We include quarter dummies into each regression model to control for the eve of the sub-prime crisis in 2007. Moreover, this allows to control for the fact that consumers tend to adopt innovations in a process over a certain time (Olshavsky 1980) which could cause a distortions in the database by consumers' hesitant use of intermediaries in the marketplace.

Table 3 presents Pearson's correlations of our independent variables. We further display Cramér's V coefficient (Cramér 1991), as it "is probably the best-known measure of association for contingency tables" (Kline 2004, p. 151). In the case of two dichotome variables, Cramér's V simplifies to the Phi coefficient [straight phi]. We see that a group's mandatory review process correlates positively with the recommendation by the group leader (Certification) and the group leader's bid. There is also a significant and positive correlation between certification and Group Leader Bid. Interestingly, we find that Group Size correlates negatively with Certification, Group Leader's Bid, and a mandatory review process. As expected, Credit Grade is negatively correlated with Amount and Homeownership. Between the variables that are used within the same regression models there is no significant correlation above 0.5 which is a first indicator that there is not any multicollinearity issue with the data. In line with our expectations, we document a significant and high correlation of 0.75 between Paid Group and Group Fee. This is not an issue since these two variables will not be included simultaneously into our multivariate analyses.

4.3 Description of data set

Our empirical analysis of financial intermediaries in the electronic P2P lending platform Prosper is based on 14,321 credit transactions between 200511 and 2007-09, covering all transactions that took place in the market in this time period. As of 2007-09-12, the marketplace consisted of a total of 385,161 registered users.

Our data set includes detailed information about these credit transactions, and there is also comprehensive information about the course of the loan-originating auction, including individual bidding and its impact on interest rates. At this point in time the data set is still heavily right censored with respect to subsequent information on ex-postrealized loan defaults. For this reason we adopt the borrowers' perspective and focus our analyses on the credit spreads realized. We begin with a closer look at the borrowers and the lenders in the marketplace. Borrowers (lenders) are defined quite literally as market participants that were involved in transactions solely as borrowers (lenders).

As we can see from Table 4, the sample of 14,321 credit transactions involved almost twice as many lenders as borrowers (32,996 vs. 16,778). This reflects the fact that on Prosper, several lenders collectively syndicate a loan in order to diversify their investments among many borrowers.

We see that nearly 90 percent of lenders in the marketplace held a stake in at least two loans, with an average bid size of 96.22 USD (whereas most of the bids amount to the minimum, resulting in a median of 50 USD). Every second lender was invested in more than 10 loans, and the average lender held 32.7 loan shares. We rarely observe repeated borrowing in the marketplace. Only 4.6 percent of borrowers have a second, and 0.2 percent of borrowers have a third loan.

Borrowers and lenders can form groups or become group members. We argue in section 2 that group leaders serve as financial intermediaries in the marketplace. Figure 4.3 presents the composition of groups by group size.

We find that, in terms of member share, groups are dominated by lenders. The share of lenders within groups ranges from 45 to 68 percent and tends to increase with group size. We calculate the share of internal financing for each group as the percentage of the last 12 months' loan amount that is provided by members of the same group. Even with an increasing group size and an increasing absolute number of lenders within a group, only a fraction of loans are syndicated within groups. With the exception of very small groups, the share of internal financing is between 0.8 and 2.2 percent. We can subsume that groups are dominated by lenders and that borrowers' loans are funded primarily by lenders outside of their group. This supports our interpretation of group leaders as financial intermediaries that produce and signal information about potential borrowers for lenders outside the group.

Table 5 presents descriptive statistics for loan amount and borrower rate by borrowers' credit grade as well as borrowers' group affiliation. Several interesting patterns emerge from this table.

Out of a total sample of 14,321 loans, 9,187 transactions were carried out by group members and 5,134 transactions without group affiliation. 58 percent of total group-affiliated borrowers were members of paid groups. We find borrowers of all

credit grades in paid and unpaid groups as well as without group affiliation. Borrowers with the best credit grades AA, A and B were in relative terms more frequent in the sub-sample of borrowers without group affiliation (40 percent vs. 25 of all group-affiliated borrowers).

The average loan amount over the total sample was 6,102 USD. Borrowers with a better credit grade borrowed higher loan amounts. We see a mean loan amount in the total sample for borrowers with the best credit grade AA of 8,264 USD whereas for "high risk" borrowers (credit grade HR) the amount was only about one third of that. The average loan amount was higher for borrowers without group affiliation. However, when comparing by credit grades, we find higher average loan amounts for group members than for borrowers without group association. When comparing unpaid and paid groups, we find that borrowers with better credit grades borrowed significantly higher amounts in groups with a paid group leader than in unpaid groups. Table 5 also lists the average borrower rate by credit grade as the spread over three-year interest rate swaps. We see that the average spread was 1,011 basis points (= 10.11 percent). Comparing borrowers with and without group affiliation, we find that average spreads by credit grade were lower for borrowers with group affiliation. Table 5 documents significant differences in average spreads between paid and unpaid groups. Borrowers in unpaid groups on average borrowed at lower spreads than borrowers in paid groups or borrowers without group affiliation. When comparing paid groups to borrowers without group affiliation, we find that borrowers with credit grades of AA, A, B and C (credit grades of D, E, and HR) borrowed at higher (lower) spreads in paid groups.

Table 6 provides sample statistics on the temporal distribution of the loan origination and the originated volume. We observe 22 transactions in the fourth quarter of 2005, and document an increase in transactions to more than 3,000 loans in the first and second quarter of 2007. We also see an increase in mean loan volume from between 4,500 and 5,000 USD in 2006 to between 6,500 and 7,500 USD in the first and second quarter of 2007. To gain a better understanding of the patterns documented in Table 5 and 6, we offer some further insights into the role of groups in the electronic lending platform in Table 7.

The first three rows in Table 7 represent the distribution of variables based on individuals' credit reports by group membership. As already presented in the last rows of Table 5, the median credit grade of borrowers without group affiliation tends to be better. Group-affiliated borrowers are on average more indebted which is reflected in the Debt-to-Income (DTI) Grade. We find an average DTI Grade of 40 percent within groups compared to 32 percent with transactions outside groups. Within the sample, 41 percent of borrowers owned a house, and homeownership was more frequent in transactions outside groups (45 percent). Average Credit Grade and DTI Grade, as well as the distribution of Homeownership seem to confirm the finding that borrowers with better risk characteristics are more frequent in the sub-sample of borrowers without group affiliation.

Regarding the characteristics of the transaction, we find that two out of three bidders within the sample reveal personal photographs (Visual SelfDisclosure). This is far

more often the case with transactions involving groups (71 vs. 55 percent). This corresponds with anecdotal evidence that group leaders often encourage group members to include personal pictures in their loan listings.

Table 7 shows that 63 percent of all transactions within the sample made use of the marketplace's auction mechanism. Borrowers with group affiliation use the auction mechanism more often (68 vs. 54 percent in transactions out of groups), and auctioned transactions are more frequent in paid groups. An auction enables lenders to bid down the interest rate and may lead to lower credit spreads for borrowers. Not using the auction mechanism may accelerate the access to credit by potentially reducing the time until a loan is fully funded, since the loan listing is closed once the required loan amount is fully funded. Facing this trade-off, borrowers outside groups decide to use the auction mechanism less often. There are two possible explanations for this finding: It could be that borrowers with better credit grades expect to benefit less from an auction of their loan listing. As an alternative explanation, group leaders might encourage borrowers to make use of the auction mechanism. Table 7 further presents the distribution of five group-related variables. Table 7 strongly indicates that group leaders create value by serving as intermediaries in the electronic marketplace in order to help overcome problems of asymmetric information. The majority (55 percent) of groups enforces a mandatory review process and commit to screening of every borrower within a group. Yet there is evidence of important differences in the role of group leaders in unpaid and paid groups. We find that certification of screened loan listings as well as group leaders' bidding is more frequent in paid groups. Group leaders assess and then observably signal borrower's credit quality (Certification) in 36 percent of transactions in paid groups compared to 24 percent in unpaid groups. Furthermore, paid group leaders bid for more than every second recommended loan listing and thus credibly signal information quality (Group Leader Bid).

Comparing the average size of unpaid and paid groups we find that unpaid groups tend to be smaller (median of 316 vs. 459 members). Finally, Table 7 presents the average costs inherent in the choice of a paid intermediary. The average fee that group leaders impose amounts to 110 basis points or about one additional percentage point of interest for the borrower. So far it is not possible to draw conclusions on the net value creation of a (paid) intermediary for the borrowers. In a next step we analyze the role of intermediation in the electronic marketplace in a multivariate set-up.

4.4 Empirical results

Table 8 presents our analyses on the role of intermediation in the electronic lending platform. In three different regression models we look at the influence of (1) general group membership, (2) the use of a paid intermediary, and (3) the hypothesized functions of intermediation on borrowers' credit conditions. The dependent variable in regression model (1) is Borrower Rate, borrowers' total loan cost including a potential group fee. This allows for the comparison of borrowers' credit spread with and without the use of an intermediary. In regression models (2) and (3), the dependent variable Borrower Rate Net excludes the group fee in order to evaluate the net effect of intermediation. Several interesting patterns emerge.

The results from regression model (1) regarding Group Affiliation as well as from

model (3) regarding the group-specific variables confirm our fundamental hypothesis H1: the use of an intermediary in the electronic marketplace significantly lowers borrowers' loan spread. Group affiliation ceteris paribus lowers the credit spread by 25 basis points. In regression models (2) and (3) we shed more light on the function and value creation of the intermediary.

Does the choice of the intermediary matter? Should borrowers make demands on paid intermediary services? In order to be able to compare the net impact of unpaid and paid groups, we analyze Borrower Rate Net in regression model (2) and find that intermediation significantly lowers borrower's cost of credit overall. However, we document a difference in the net impact of group membership of 42 basis points: An unpaid intermediary reduces borrower's credit spread by 107 basis points, a paid intermediary by 65 basis points. It follows that the group fee can turn the case for a paid intermediary borderline. The average group fee of 110 basis points (Table 7) will more than counter the net reduction in credit spread. Taken together, intermediation has a positive net impact but the choice of intermediary matters. We hereby do not comment on the overall impact of paid groups, since this analysis does not incorporate the intermediary's role in overall access to credit or the long-run performance of the loan thus originated.

In model (3) we analyze in greater detail how the intermediary creates value for the borrower. Again, we look at the net impact of intermediation (Borrower Rate Net) and control for the fee of a paid intermediary (Group Fee). All group-specific variables in regression model (3) have significant impact on credit spreads. The variables Certification and Group Leader Bid in regression model (3) significantly reduce borrowers' loan costs. Hypotheses H2 and H3a cannot be rejected: An important function of the intermediary is the screening of a potential borrower. The intermediary may then recommend the borrower's credit listing. There is further evidence for the hypothesized creation of value by the intermediary: We find significant lower credit spreads in groups where the group leader was committed to screening every potential borrower (Mandatory Review).

Regression model (3) also shows that "actions speak louder than words": the group leader's bid for the borrower's credit listing exerts a significant stronger impact on borrowers' credit conditions than a recommendation. Moreover, Certification is only significant at the 10-percent level. We can confirm Hypothesis H3b: the regression coefficient of Group Leader Bid exceeds Certification.

We find that a group's reputation serves as a proxy for the future diligent assessment of borrowers by the group leader. Lenders increasingly bid down the interest rate in groups with a good reputation, resulting in lower credit spreads for borrowers. Hypothesis 4 cannot be rejected as a group's reputation (Group Rating) significantly lowers borrowers' spread. Table 8 shows further evidence for the negative effect of group size on loan spreads. This finding statistically confirms our hypothesis H5. We find evidence for a perceived lower credit risk within larger groups due to more effective peer-monitoring. When comparing estimated coefficients, we see that this effect is far less important than the group leader's role in screening and monitoring of borrowers. This can be regarded as a certain restriction in the confirmation of H5 from

an economist's point of view. Nonetheless, it confirms the important role of the group leader as the financial intermediary. An analysis of the effect of intermediation with credit grade sub-samples in Table 9 confirms our main findings and yields some interesting additional insights. Controlling for borrowers' risk characteristics and the group fee we find that intermediation may significantly reduce borrowers' credit spread and that the reputation of a group has a strong impact across credit grades. Interestingly, a mandatory review process, the recommendation of a loan listing by the group leader, or a group leader's bid on a screened loan listing have a significant impact mostly for borrowers with lower credit grades "D", "E", and "HR". These credit grades represent 57 percent of all group members. This finding highlights that the intermediary may create significant value by screening and monitoring of borrowers who represent more risky investments. We conclude that intermediation is particularly valuable for borrowers with less attractive risk characteristics.

Overall, our results show that even though the electronic P2P lending platform leads to disintermediation by enabling the direct brokerage of loans between borrowers and lenders, a new type of financial intermediary emerges. Market participants become group leaders and provide intermediary services, reducing the information asymmetries prevalent in the electronic marketplace. The intermediary primarily creates value by screening potential borrowers with lower scores, representing investments with higher risk a priori. This finding is supported by the significant reduction in borrowers' credit spread by a mandatory screening process as well as the intermediary's recommendation of a borrower (Certification). Moreover, bidding on the screened borrower's credit listing has an even stronger impact on the resulting spread. Given a mandatory screening process, the recommendation of a borrower and the group leader's bid for the recommended loan listing, the credit spread will ceteris paribus be 156 basis points lower (see model 3 in Table 8). This more than compensates for the average required fee of 110 basis points (as shown in Table 7). These results are stable when controlling for borrowers' credit history as well as transaction characteristics. In all regression models in Table 8 and 9 we find that the variables based on individual credit reports significantly influence credit conditions, and that a borrower's Credit Grade as a proxy for Probability of Default has the strongest impact. In model (1) in Table 8 for example, we find that ceteris paribus a decline in credit grade by one grade is associated with an average of 280 basis points increase in credit spread. Increasing indebtedness (DTI Grade) which proxies for Loss Given Default or a higher loan amount (Amount) significantly increases credit spread. We cannot find a consistent significant impact of Homeownership. This result is intuitive since a house does not serve as collateral for loans on the Prosper marketplace. Hence, homeownership does not per se serve as information on borrowers' creditworthiness. There is initial evidence that this somewhat changed in the climax of the sub-prime crisis (Crowe and Ramcharan 2009), which exceeds our period of analysis.

We further control for use of the auction mechanism (Auction) and find a significant and negative impact on credit spreads. One obvious interpretation of this result is that the auction mechanism allows for competition among bidders which improves the

conditions for borrowers. Another possible interpretation is that not using the auction mechanism serves as a negative signal of creditworthiness where the marketplace requires a significant risk premium for loan listings that are not auctioned.

4.5 Robustness tests

4.5.1 Tests for self-selection bias

Economic agents participating in capital markets are subject to self-selection (Alexander, Jones, and Nigro 1997). Self-selection arises if those participating in an activity are systematically different from those who do not participate (Bjorklund and Moffitt 1987). Each OLS regression analysis involving any such participation (including voluntary group memberships) can suffer from an endogeneity bias through the existence of variables that simultaneously influence the decision to choose the group membership as well as the dependent variable (Heckman 1979, Rubin 1979).

In financial transactions, self-selection in choosing an intermediary may arise from different levels of expertise as well as transaction characteristics (Alexander, Jones, and Nigro 1997, Zumpano, Elder, and Baryla 1996). Self-selection might be present in electronic marketplaces when individuals turning to intermediaries might differ significantly from those not using intermediary services. This could be the case if, for example, borrowers with weak market access, i. e. bad credit history resulting in a low credit score, use group membership as mitigation. Transaction characteristics like loan amount might also lead to self-selection towards using intermediary services. One possible way of easing the problem lies in the matching method (Rubin and Waterman 2006), by which one can construct pairs of comparable credit transactions with and without using intermediaries. Pairs are selected from both groups that do not differ in their relevant characteristics, i. e. they are identical ("statistical twins") or close to identical. It is most relevant for creating the pairs that the characteristics are linked with the relevant measure which determines the outcome of the credit transaction. Due to the similarity of the pairs with and without using intermediaries one can assume that self-selection bias can be excluded from the analysis.

As there are usually a lot of explanatory variables on interest rates (Avery, Calem, and Canner 2004), we need to find adequate partners matching in terms of several variables. This is also a multi-dimensional problem, which complicates the search for adequate partners for every credit transaction. As a solution, Rosenbaum and Rubin (1983) propose the use of a balancing score, i. e. a function of all relevant characteristics. The matching partners selected are similar with respect to that balancing score. The propensity score, as a special balancing score, equals the probability of using a group as an intermediary. The propensity score is usually determined using Logit or Probit models (Titus 2007).

When constructing matching pairs, all relevant characteristics of the customer are implicitly taken into consideration by the propensity score. Therefore, when searching for the matching partner, one has to consider only one dimension in terms of the propensity score (D'Agostino 1998). An indepth discussion of the method as well as adequate search algorithms is provided by Gensler, Skiera, and B?hm (2005) and Titus (2007), the method has recently become established in finance research (e.g.,

Drucker and Puri 2005).

Table 10 presents our estimates for the matched sub-sample, where we used a Logit model for propensity score estimation. Controlling for selfselection, all regression model estimates remain largely unchanged and significant. We do not find any support for a self-selection-driven estimation bias through the use of intermediary services in the electronic credit marketplace which corroborates our prior findings.

4.5.2 Tests for multicollinearity

The estimates from our multivariate analyses could be inaccurate in the presence of multicollinearity. As a first counter-argument we see from Table 3 that there are no unusually high pairwise correlations between our independent variables. To address residual concerns about multicollinearity, we calculate the variance-inflation factors (VIF) (Freund and Littell 2000, p. 98) for our models. As we see in our analysis (e.g., Table 11 presents the collinearity diagnostics for the third regression model in Table 8), there is no evidence of a high inter-correlation. As an additional measure of collinearity, we present the Condition Number in the same table. Belsley, Kuh, and Welsch (1980) propose 10 as a beginning and 100 as a serious point where collinearity affects estimates, Paris (2001) recognizes "deleterious effects when Belsley's Condition Number is around 30". Therefore, our Condition Number of 14.737 indicates only a very small degree of multicollinearity.

4.5.3 Tests for fixed effects

As market participants are typically involved more than once in the transactions on the market place, one can assume that fixed effects may exist that could distort the results of our analysis. Therefore, we conduct an additional robustness test on this issue.

Potential borrower fixed effects: As borrowers are on average involved in 1.05 transactions (see Table 4), we cannot assume that there are fixed effects.

Potential lender fixed effects: Lenders, in contrast, are involved in 32.07 transactions, whereas the general existence of such effects on the market place cannot be rejected a priori. Nevertheless, our hypotheses and the variables employed in our multivariate analysis in this article do not cover lender-specific aspects; hence this issue is not of concern in this context.

Potential group fixed effects: Lenders and borrowers organize in groups, which indeed might be a source of unobserved effects, although we cover a lot of group-specific variables in our regression models. This lowers the potential distorting impact of any unobserved effects. Nevertheless, we test for the robustness of our primary analysis by estimating a fixed effect model for our non-groupspecific variables (see Table 12). We observe a decrease in degrees of freedom, naturally, but the coefficients of our control variables do not change signs and keep rather stable.

4.5.4 Tests for operationalization of variable credit grade

The credit grade represents the variable of highest influence in all regression models. As introduced in section 4.2, this variable represents the credit grade which is provided by an external rating agency. In their credit scoring model, 40 score points are equivalent to one notch on the credit grade scale. Therefore, credit grade can be included as a metric variable in our regression models. We control for residual

concerns that our results might be primarily driven by the operationalization of this variable by conducting several robustness tests. First, we conducted the same regression models with dummy variables for each credit grade, which produced the same results in terms of coefficients, t-statistics, and goodness-of-fit measures (not displayed here, therefore). Furthermore, we operationalized the credit grades with the corresponding historical probabilities of default that were published by Experian (Prosper Marketplace Inc. 2007a) as displayed in Table 13. The results of these regressions are shown in Table 14. Again, the results are comparable to the analyses in Table 8. In regression model (3), Certification is no longer statistically significant, which is in line with our overall finding, that "actions speak louder than words". Nonetheless, our overall results remain unchanged.

4.5.5 Tests with sub-samples per year

In unreported results, we conducted our regression analyses for the sub-samples of the transactions of 2006 (a total of 5,440 loans) and of 2007 (8,189 loans). We can address residual concerns that temporal effects such as the eve of the sub-prime crisis in 2007 bias our results: estimates for these subsamples remain basically unchanged to the results presented. There is recent evidence for an impact of the subprime crisis beginning with the fourth quarter of 2007 (Crowe and Ramcharan 2009), which is beyond our period of analysis.

4.5.6 Tests with robust standard error erstimation

To check the robustness of our models against some common types of misspecification, we applied a regression with Huber/White/Sandwich estimation (Kent 1982)("regression with robust standard errors"). The estimation results show exactly the same coefficients, as expected by definition. More important, there is not a relevant change in t-ratios, i.e., all levels of significance remain unchanged (therefore, table not shown here).

5 Conclusions

This paper presents new empirical evidence on electronic markets for consumer loans. We analyze the role of financial intermediaries in the American electronic P2P lending platform https://www.wendangku.net/doc/db7081318.html,.

The marketplace enables the direct mediation of loans without an intermediary between borrowers and lenders. Our analysis of an electronic credit lending platform provides differentiated results: Wefind that there are participants in the electronic marketplace acting as financial intermediaries, and that intermediation services significantly improve borrowers' credit conditions.

We apply a new data set to analyze theoretical predictions from the literature on financial intermediation and find that, as suggested by traditional intermediation theory, the intermediary creates value by reducing information asymmetries between borrowers and lenders. We document the positive impact of the intermediary's screening activities for the borrowers. When looking at credit grade sub-samples, we recognize these effects predominantly for borrowers with less attractive risk characteristics. Based on superior private information, the recommendation of a credit listing significantly improves borrowers' credit conditions. Moreover, the intermediary's bid has an even stronger impact on the resulting credit spread. Our

results indicate that intermediation costs could be more than compensated for by lower credit spreads for borrowers. Borrowers should also consider the intermediary's reputation. These results are robust to self-selection regarding the choice of an intermediary as well as other control variables relevant to the electronic lending platform. Based on our analyses, one can quantify the effect of each possible listing feature on the credit spread.

Our results contribute to the existing literature on financial intermediation and electronic credit markets and yield some interesting implications for the setup of online credit lending platforms and the behavior of their participants. However, the deduction of broad conclusions from our study is limited in so far as our sample is restricted to individuals who chose to participate in the marketplace. Additional data on market participants' banking relationships and an assessment of the individual alternative cost of credit would enable the evaluation of the overall macro-economic impact of internet-based lending platforms such as the displacement or crowding out of traditional banks, or industry-economic effects such as the reactions of traditional banks in response to these online platforms. Also, we focus mainly on the impact of intermediation on borrowers' credit conditions. It would be interesting to include ex-post-realized loan defaults into further analyses. Moreover, our data sample consists of consumer credit transactions on an American marketplace. Generalization to electronic markets for corporate or governmental debt, to other electronic markets, and to markets in other countries may provide interesting avenues for future research.

二、文献综述

P2P 网络借贷文献综述

摘要

随着互联网金融的迅猛发展，作为民间借贷网络化的P2P 网络借贷这一新型金融服务模式日渐突起。P2P 网络借贷在对我国金融市场有所补充的同时，也带来了各种问题。本文首先介绍了P2P 网络借贷的概况及其在我国的发展现状，后从文献梳理的角度，综述了现有研究成果，指出未来研究的重点与方向。

关键词：P2P 网络借贷；风险；监管

一、引言

P2P网络借贷（Peer-to-Peer lending）是一种近年来逐渐兴起的个人对个人直接信贷模式（谢平，2012）。网贷公司通过线上平台撮合借贷双方达成交易。平台本身扮演信息中介的角色，提供信息披露、信用评级、资金结算、逾期催收等服务，平台利润主要来源于客户缴纳的手续费。2005年，ZOPA作为首家针对个人的小额融资网站在伦敦上线，拉开了P2P网络借贷的序幕。经过10年运营，总计撮合了7.5亿英镑的网络贷款。美国平台Prosper2014年的累计成交额约为25亿美元。P2P网贷在英美等国家早已成为除了储蓄和传统投资渠道外的另一种选择（Slavin，2007）。欧美实践的成功让P2P网贷逐步走向世界。

P2P网贷在中国发展十分迅速，它的出现填补了小额借贷市场的空白。2013年中国P2P网贷行业迎来爆发式增长。据网贷之家统计，仅2013 年全国共出现约800家网贷平台，贷款存量增加至268 亿元，其高速增长的势头一直保持至今。网贷行业的高速发展引起了学术界的广泛关注，吸引了包括经济学、金融学、管理学与心理学等多领域学者共同研究。

二、P2P 网络借贷发展现状

P2P 是英文Peer-to-Peer 的缩写形式，代表着Peer-to-Peer Lending，即“个人对个人借贷”。P2P网络借贷平台就是个人对个人借贷与网络借贷相结合的金融服务平台。P2P 网络借贷的本质就是民间借贷网络化，它不仅是基于互联网的，而且具有门槛低，收益高，操作简单，手续便捷，交易透明等特点。

P2P 网络借贷基本属于小额借贷。随着互联网普及和技术的迅速发展，小额信贷的服务范围逐渐扩展，演变成“线上”和“线下”并存的模式，其中“线上”模式就是P2P 网络借贷平台。2005 年3 月，全球首家P2P 网络借贷平台Zopa 在英国伦敦成立。2006 年，美国第一家P2P 网络借贷平台Prosper 网站诞生，Prosper 的经营模式是比较单纯的借贷中介模式，一般是出售平台服务并收取服务费用。然而作为美国最大P2P 平台的Lending Club，它则是建立在网络的高传播特性及人与人之间的信任度，通过社交网络将贷款人和借款人结合起来。

P2P 网络借贷在英、美等国发展日趋成熟的同时，也进入了我国的金融体系。从国内的投融资环境来看，正规金融体系忽视对中小微企业的借贷业务，再加上2008 年金融危机的影响，中小微企业融资更是雪上加霜。P2P 网络借贷模式迎合了这类融资需求，因此，在我国迅速发展起来。2007 年 6 月，拍拍贷（https://www.wendangku.net/doc/db7081318.html,）在上海成立，标志着P2P 网络借贷平台在我国正式登陆。随后几年，P2P 网络借贷在我国进入缓慢发展阶段。直到2012 年，我国P2P 网络借贷平台呈现爆发式增长。据《中国P2P 借贷服务行业白皮书2014》统计，至2013 年低，我国P2P 借贷平台的数量约为700家，年交易额约为 1 100 亿元，较2012 年的100亿元增长了10 倍之多，行业累计投资者达100 万人左右。据网贷之家数据显示，到2014 年12 月末，我国P2P 网络借贷平台数量已达 1 575 家，总成交量为2 528 亿元。

三、P2P网络借贷模式研究

（一）国外主要模式

国外平台基本分为两类：营盈型和非营利型(Ashta & Assadi,2009)。这里的“营利”是指平台投资人的投资目的。盈利型平台的投资人，希望通过借出资金来获取匹配风险的经济收益。非盈利型平台的投资人，其投资行为旨在帮助他人，不注重索取经济回报。现今多数盈利型平台都在本国范围内开展业务，服从所在国的监管要求(Berger&Gleisner,2009)。非盈利型平台一般不受地域限制，可在全球范围内运营（Xu ,2010）。在此基础上辛宪（2009）将国外典型平台细分为三类: 公益型、单纯中介型和复合中介型，后两类都属于盈利型平台。公益型平台其服务对象主要为欠发达地区的低收入者。单纯中介型平台只扮演信息中介的角色，不干涉用户交易过程。复合中介型平台提供信息服务的同时还要充当监督者、联合追款人、利率制定者等角色。代表平台如下：

（1）公益型Kiva。Kiva (https://www.wendangku.net/doc/db7081318.html,)成立于2005 年，是一家组织欧美富裕阶层投资人向发展中国家中小企业提供贷款的非营利型P2P网贷平台（张正平，2013）。其运营资金基本由募捐获得，为小微借款人提供低利率贷款与免费的中介服务。由于各国法律政策的不同，Kiva的业务开展需要与当地小额贷款机构(MFI)合作，通过其作为中间人来监督贷款的发放与偿还(Ashta & Assadi,2009)。（2）单纯中介型Prosper。Prosper (https://www.wendangku.net/doc/db7081318.html,)在借贷交易中只起单纯的信息中介作用，通过信息披露与信用评级为借贷双方自由选择提供依据，交易达成后Prosper不再介入借贷交易本身。在Prosper平台凡拥有社会保障号、个人税号、（完整内容请到百度文库）银行账号且个人信用评分超过520分的美国公民

P2P 金融下的中小企业融资互联网金融外文文献翻译最新译文

文献出处: Waitz M. The small and medium-sized enterprise financing under P2P finance [J]. Journal of Business Research, 2016, 8(6): 81-91. 原文 The small and medium-sized enterprise financing under P2P finance Waitz M Abstract Small and medium-sized enterprise financing difficult is worldwide difficult problem. Article introduces the concept of the Internet financial, mainly summarized the P2P financial in the development of financial innovation and integration of science and technology, a combination of academic research on P2P financial now of the five directions of various views and opinions. Points out the current P2P financial problems in the development of risk control, and analyses the trend of the Internet financial. Keywords: P2P financial; Financial innovation; Risk control 1 Introduction Look from the history of enterprise development, a large enterprise originate from small and medium-sized enterprises. Small and medium-sized enterprises (smes) is the most dynamic part of the national economy, often walk in the forefront of technology development, in the high-tech industry, clean energy, green economy, etc, have good performance, play an important role in the economic transformation. Small and medium-sized enterprise financing difficult is worldwide difficult problem. These small and medium-sized enterprise financing environment and narrow channels, more than 60% are unable to get a bank loan. At present, science and technology enterprises and the characteristics of light assets, financing difficulties, become a huge bottleneck of sustainable development. 2 The concept of the Internet financial In the past two years, the Internet financial show explosive growth, since 2014, the Internet financial sector performance strength. Current economic field exists the phenomenon of two special contradiction, one is the small and medium-sized

互联网金融发展文献综述及外文文献资料P2P金融

本份文档包含：关于该选题的外文文献、文献综述 一、外文文献 标题: Online brokers lead the way for French internet finance 作者: Caffard, Christophe 期刊名称: International Financial Law Review 卷: 20；期: 3；页: 20-24 Online brokers lead the way for French internet finance 1 Regulated brokers Regulated brokers are legal entities which have an investment services licence and are subject to the prudential regulations of the Comite de Reglementation Bancaire et Financiere (CRBF) and the Conseil des Marches Financiers (CMF). * Choice of legal form: regulated brokers are not required to be incorporated in a specific legal form; however, under article 13 of the MAF Law, the CECEI checks whether the legal form of the brokerage company is appropriate for providing investment services. In practice, any type of commercial company is admitted: societes de capitaux (limited companies) or societes de personnes (partnerships). The formalities of share transfer, tax and the scope of liability of a company's management will be relevant factors to the choice of legal form. * Application for an investment services licence from the CECEI: the most important part of the application is the description of the investment services, and a business plan including prospective financial statements for the following three years. The CMF will check whether the business plan is consistent with the investment services licence requested by the broker. The CECEI will ensure that the applicant's own initial funds are consistent with the business plan. The scope of the investment services licence is variable and covers one or more ofthe following investment services: Reception and transmission of orders to another investment services provider on behalf of investors, for execution. This is the core investment service provided by the

互联网金融外文翻译银行业是必要的而银行不是,互联网时代的金融中介的未来

译文 银行业是必要的而银行不是，互联网时代的金融中介机构的未来 摘要 本文探讨了互联网时代下金融机构和银行作为特殊的金融机构的未来可能是怎样的问题。由于互联网而导致的交易费用的减少会降低进入金融产品市场的壁垒，因为有可能不再需要运行成本密集型的分支的大型系统。但是，对金融机构的职能研究表明，不是每个人都可以销售和经销金融产品。这是真的，因为金融业务中的信息不对称问题需要一个拥有良好信誉的中介，也因为需要限制大型资本基金转换资产的风险。这两个要求变现了进入金融中介市场的重要壁垒。并不是每一个金融产品会因为互联网的崛起而将面临更多的竞争，只有那些标准化和低风险的产品。此外，那些拥有可观资本和良好声誉的大公司可能被视为银行的新竞争者。 关键字：银行业，银行，金融机构，互联网 1、引言 “银行业是19世纪的钢铁行业。”当谈到关于新的信息技术对银行的影响的谈论时，这句话经常被提起。更一般来说，可能有人会问，新信息技术是如何成功的，特别是互联网的，可能会改变商业和金融机构的市场情况。在互联网的帮助下，人们可以执行所有银行的业务，而不需要银行。这意味着传统银行分支机构的中介。此外，互联网已经使客户直接从网上购买股票而不需要访问当地的分支银行。从更广泛的意义上来说，在互联网的帮助下，金融市场的供给和需求可能通过互联网满足，而不需要金融中介机构。互联网的崛起是否真的是金融中介机构的威胁？在急剧减少的交易成本情况下，商业和竞争将如何变化？本文考察了互联网的成功对金融机构和银行的影响。 2、金融机构的发展 几个世纪以来，许多金融交易需要个人的存在。随着现代信息技术的发展，这些都被改变了。如今，客户可以在不进入当地分支银行的情况下进行任何金融交易。转移支付和支付账单可以通过网络进行，个人金融交易以及关于金融问题的信息咨询业可以通过网络进行。此外，互联网创新类似智能卡或者其他电子支付系统消除了为了得到一些现金而访问分支银行的需要。 但是互联网也会改变咨询活动：在许多情况下，它甚至可能减少个人存在的

互联网金融文献综述

互联网金融发展中的风险管理研究文献综述 一、互联网金融发展中的风险管理研究的写作意义 互联网金融作为互联网、移动互联网与金融相结合的新型模式，正在以飞快的速度改造着传统金融业。一方面，互联网企业借助电子商务和信息数据等优势大举进军金融领域；另一方面，传统金融机构加速互联网业务创新，加强与互联网企业的合作。互联网金融日新月异的创新与变革使我们面临着不同于传统金融的新的金融风险。认识互联网金融风险产生的原因和特点，对于健全和完善互联网金融风险的防范和管理机制，发挥互联网金融对经济发展的良性促进作用是十分必要的。 二、互联网金融发展中的风险管理国内研究现状 （一）我国互联网金融发展中的风险管理现状 互联网金融是金融与互联网的融合，是在互联网时代，实现资金快速配置与融通的新金融模式。目前，国内互联网金融除了传统的网上银行功能外，主要形式有金融理财产品的网上销售、网络保险产品销售、网络证券买卖、第三方电子支付和网络小额信贷等。以余额宝为例，自2013年6月13日支付宝推出新产品余额宝以来，余额宝的业务量已经发展到较大规模，而且显现出强劲高速的发展势头，其他互联网金融也呈现突飞猛进发展趋势。 目前互联网金融的发展没有，也很难改变金融行业高风险的属性，在优化资源配置效率、提升金融效率的同时，也带来了不少的风险。从2013年以来，我国互联网金融风险开始暴露出来，案件频发，消费者权益受到侵害的事件时有发生，在2013年10月到11月末，就有39家P2P借贷平台陷入困境和倒闭。（二）专家、学者对互联网金融发展中的风险管理问题及对策分析 乔玉梅（2014）指出，随着信息技术和网络技术的普及和应用，互联网金融对银行业沿袭多年的经营范围、运作模式、管理理念和竞争格局提出了前所未有的挑战，深入研究互联网金融风险与监管问题，保证互联网金融业务的健康运行、避免各种风险带来的损失，对我国互联网金融业务发展具有重要的理论和实践意义。[1] 朱治豪（2014）指出，互联网金融在中国发展的如火如荼，扩大了金融服务的边界和市场。但是互联网金融业有相应的信用风险、市场选择风险、技术风险和法律风险，这些风险都加大了金融市场不稳定的可能性。故互联网金融企业应加强安全体系、风险控制体系建设，政府加大立法力度，共同防范互联网金融

互联网金融对传统金融业的影响外文文献翻译

互联网金融对传统金融业的影响外文文献翻译(文档含中英文对照即英文原文和中文翻译) 译文： 互联网金融对传统金融业的影响 摘要 网络的发展，深刻地改变甚至颠覆了许多传统行业，金融业也不例外。近年来，金融业成为继商业分销、传媒之后受互联网影响最为深远的领域，许多基于互联网的金融服务模式应运而生，并对传统金融业产生了深刻的影响和巨大的冲击。“互联网金融”成为社会各界关注的焦点。 互联网金融低成本、高效率、关注用户体验，这些特点使其能够充分满足传统金融“长尾市场”的特殊需求，灵活提供更为便捷、高

效的金融服务和多样化的金融产品，大大拓展了金融服务的广度和深度，缩短了人们在时空上的距离，建立了一种全新的金融生态环境；可以有效整合、利用零散的时间、信息、资金等碎片资源，积少成多，形成规模效益，成为各类金融服务机构新的利润增长点。此外，随着互联网金融的不断渗透和融合，将给传统金融行业带来新的挑战和机遇。互联网金融可以促进传统银行业的转型，弥补传统银行在资金处理效率、信息整合等方面的不足；为证券、保险、基金、理财产品的销售与推广提供新渠道。对于很多中小企业来说，互联网金融拓展了它们的融资渠道，大大降低了融资门槛，提高了资金的使用效率。但是，互联网金融的跨行业性决定了它的风险因素更为复杂、敏感、多变，因此要处理好创新发展与市场监管、行业自律的关系。 关键词：互联网金融；商业银行；影响；监管 1 引言 互联网技术的不断发展，云计算、大数据、社交网络等越来越多的互联网应用为传统行业的业务发展提供了有力支持，互联网对传统行业的渗透程度不断加深。20世纪末，微软总裁比尔盖茨就曾断言，“传统商业银行会成为新世纪的恐龙”。如今，随着互联网电子信息技术的发展，我们真切地感受到了这种趋势，移动支付、电子银行早已在我们的日常生活中占据了重要地位。 由于互联网金融的概念几乎完全来自于商业实践，因此目前的研究多集中在探讨互联网金融的具体模式上，而对传统金融行业的影响力分析和应对措施则缺乏系统性研究。互联网与金融行业一向是风险

互联网金融安全中英文对照外文翻译文献

互联网金融安全中英文对照外文翻译文献 中英文对照外文翻译文献(文档含英文原文和中文翻译)

Database Security in a Web Environment Introduction Databases have been common in government departments and commercial enterprises for many years. Today, databases in any organization are increasingly opened up to a multiplicity of suppliers, customers, partners and employees - an idea that would have been unheard of a few years ago. Numerous applications and their associated data are now accessed by a variety of users requiring different levels of access via manifold devices and channels – often simultaneously. For example: ? Online banks allow customers to perform a variety of banking operations - via the Internet and over the telephone – whilst maintaining the privacy of account data. ? E-Commerce merchants and their Service Providers must store customer, order and payment data on their merchant server - and keep it secure. ? HR departments allow employees to update their personal information –whilst protecting certain management information from unauthorized access. ? The medical profession must protect the confidentiality of patient data –whilst allowing essential access for treatment. ? Online brokerages need to be able to provide large numbers of simultaneous users with up-to-date and accurate financial information. This complex landscape leads to many new demands upon system security. The global growth of complex web-based infrastructures is driving a need for security solutions that provide mechanisms to segregate environments; perform integrity checking and maintenance; enable strong authentication and non-repudiation; and provide for confidentiality. In turn, this necessitates comprehensive business and technical risk assessment to identify the threats,

互联网金融文献综述

互联网金融的理论与实践 1全球金融发展及趋势 1.1 全球金融格局 从最开始的“高利贷”开始，金融业经过长时间的历史演变，从朴素的实物（货币）借贷，逐步扩展为由多种要素组合而又相互作用的庞大系统，包括货币资金的筹集、分配、融通、运用及管理等。在此过程中，金融行业的专业性和集中度日益提高，大型金融机构不断形成，导致金融权力逐渐集中。 这一权力集中的趋势不但反映在一锅部，同样反映于各国在全球金融市场上的地位。从16~17世纪荷兰的崛起，从18~19世纪英国金融帝国的建立，再到20世纪至今美国金融主导权力的形成，国际金融格局的历史演变表明，一国的金融力量在国际竞争中居于至关重要的地位。一方面，一国强大的金融力量将极支撑该国经济的发展，促使该国在国际竞争中居于领先地位；另一方面，一国领先的经济实力将促进全球性金融资源向该国的集中，凸显该国在全球金融格局中的主导地位。二者互相促进，导致国际间金融资源的过度集中。 全球金融资源在少数发达国家的过度集中导致国际围的金融失衡——发达国家的金融产品过于丰富，金融市场急剧膨胀，虚拟经济大幅度脱离并超过尸体经济，累积巨大的系统风险，并把风险向全球围扩散。而不发达国家基本的金融需求得不到满足，加之本国金融体系的抗风险能力差，有限的外汇资金无法在本国市场得到好的回报，只能回流美国购买美国国债或机构债券等金融资产，不但削弱了本国的金融资源、抑制了本国的金融需求，而且分担了发达国家的金融风险。 经济全球化与金融全球化的发展非但没有改善这一状况，反而导致情况的恶化，加重全球经济失衡。2007年的金融危机之后，国际金融格局有所变化，全球金融体系正经历着结构性变革。全球开始质疑美元的单一世界货币体系的正当性，改变现有金融国际秩序和规则的呼声强烈。亚洲和新兴市场银行在全球的重要性日益提升，国际货币体系和国际金融市场有向多元化发展的趋势，美国和欧盟的金融地位均有所下降，但其住到地位并非发生根本性动摇，其弊端也无从消除。

互联网金融外文翻译文献

文献信息： 文献标题：INTERNET FINANCE: DIGITAL CURRENCIES AND ALTERNATIVE FINANCE LIBERATING THE CAPITAL MARKETS（互联网金融：数字货币和替代金融解放资本市场） 国外作者：Kim Wales 文献出处：《Journal of Governance and Regulation》, 2015,4(1):190-201 字数统计：英文2505单词，13427字符；中文4405汉字 外文文献： INTERNET FINANCE: DIGITAL CURRENCIES AND ALTERNATIVE FINANCE LIBERATING THE CAPITAL MARKETS Abstract This article discusses how the sudden shift in policy reform and innovation has the potential to liberate the financial markets. The economic potential of internet finance is beginning to take hold across the capital markets as industries like Peer–to–Peer Lending, Equity and Debt based Crowdfunding and virtual currencies and cryptocurrencies which are types of digital currency are quickly transforming the way businesses are being financed. From borrowing and lending, buying and selling securities, to conducting wire transfers internationally, these innovations are creating a new class and generation of investors will source investments opportunities. Helping institutions and governments assess risks and manage performance in order to determine where to deploy capital; and showing signs of lessening the inequality gap. Following the neolithic agricultural revolution and the industrial revolution, this new revolution will enable more people to access financial services in less traditional ways, especially the unbanked world with its huge potential. These new financial opportunities, such as peer –to - peer (P2P) lending, will be discussed and examined, and we will stress how they can allow people to bypass

互联网金融的发展与监管(文献综述)

互联网金融的发展与监管（文献综述） 国外研究现状： 国外早已出现了各类互联网金融业态，由此也就自然导致了国外很早就对互联网金融监管的讨论。早在1983年，Hirshleifer(1983)就发表了一篇文章，明确把第三方支付界定为“俱乐部商品”。他以一个形象的故事说明了这一点：假定有一个处于低地地带的“拓荒地”，经常有海水倒灌风险，所以，每家都需要筑一段堤坝，而整块“拓荒地”的整体安全程度，并不等于每家为筑坝所付出努力之总和，而是取决于堤坝最薄弱那家所付出的努力。 Varian(2004)则进一步把这个模型向前推进了一步，由于每家在拓荒地的身家多少不同，这就会造成每家投入筑坝的努力不一样，比如，一贫如洗的光棍可能就没有动力去认真修堤坝。那么，这个光棍的行为就构成了整个“拓荒地”安全的威胁，在每家各管一段的无政府状态下，最终这个“拓荒地”就会有灭顶之灾。这个时候，就需要有人充当监管者，确保这个光棍的堤坝必须达到最低的安全标准。他们的研究，奠定了对互联网金融有必要进行监管的理论基础。在互联网金融的现实发展及学者们的理论研究之后不久，互联网金融的监管问题就进入到了美国监管者们的视野之中。 2008年9月，波士顿联储和亚特兰大联储写了一篇名为《理解新型零售支付中的风险》的文章，该文认识到零售支付正在从纸质交易向非现金（noncash）支付转变，由此带来不少新型零售支付业态的出现（PayPal已明确进入了本文的视野），这会带来五类风险，即欺诈风险（Fraud）、操作风险（Operational）、法律风险（Legal）、清算风险（Settlement）、系统性风险（Systemic）。 其中，与其他传统支付相比，移动支付因其匿名性、基于公共网络而使得数据安全（未经授权的修改、损坏和数据泄露）和非法使用（洗钱、资助恐怖主义、购买非法商品和服务）问题显得尤其突出。虽然截止到2012年，美联储仍认为，移动支付并非全新支付体系，而不过是一些借助新的通讯工具接入传统平台的新方式而已，将现有对ATM、信用卡等的监管规则扩展并覆盖它们即可，暂时尚无需全新立法。但是，他们在2012年8月报告中却强调了互联网金融发展的一条基本原则：“对现有监管政策的了解，应当成为业内常识。”的确，在美国，仅就现存监管体系和监管法规适用于互联网金融而论，其也仍然是比较繁杂且严格的。 从监管体系来说，美国可能对互联网金融企业行使监管权限的机构众多。如果不属于金融机构，主要由联邦通讯委员会和联邦贸易委员会负责监管；如果属于金融机构（特别是银行），因美国联邦和州的双层金融监管体系的存在，属于联邦管辖的，则消费者金融保护局（2010年之后依据多德-弗兰克法案之后成立的机构）、美联储、联邦存款保险公司、货币监理署、美国证监管等都可能对其从不同角度施加管辖权；属于州政府管辖的，则在美国绝大多数州，其运作都需提前取得许可，并按牌照规定范围经营。从需遵守的监管法规来说，美国虽无专门针对互联网金融的新立法，但现有监管法规已较繁复。到底使用哪些规则，则需要根据不同业态、机构在从事业务中实际上所扮演角色的不同来权衡。 国内研究现状： 20世纪80年代，中国银行业开始全面使用计算机，金融电子化时代来临。90年代初，我国金融专用网络体系建设获得了较大发展。1993年，中国政府宣布将金卡等一系列“金”字工程作为重要的国民经济信息化工程后，我国金融电子化建设进程加快，并取得了巨大的成就。我国国内大部分城市中，本地清算系

互联网金融电子银行外文文献翻译2014年译文3050字大数据

文献出处:RICHARD C. Internet Finance's Impact on Traditional Finance [J]. The Journal of International Finance, 2014, 6(12): 13-29. （本译文归百度文库所有，完整译文请到百度文库） 原文 Internet Finance's Impact on Traditional Finance RICHARD C Abstract With the advent of the era of web2.0, Banks have full access to the Internet age, the large data of the Internet is profoundly affects the future of the Banks, represented by the Banks of financial enterprises and represented by electric business enterprise Internet companies, is a financial of the Internet and the Internet financialization swing. The large data of the Internet is profoundly affecting the bank's future development direction, it also marks a new era of financial, the rapid development of Internet financial, also brought unprecedented challenges to electronic banking, regardless of technical strength, talent resources level, or the problem such as system innovation are all faced with great challenge and opportunity. Key words: Internet financial; Electronic banking; Third-party payment; big data 1 The concepts of Internet financial Internet finance is the product of the combination of both financial and Internet, is to use the Internet technology and mobile communication technology to realize capital flow and information transfer mode of new financial, Internet financial is different from traditional finance: financial business adopted by the media, financial participants direct contact through the Internet, make the transparency of financial business, higher intermediate cheaper, more convenient way. The current Internet financial landscape, is made up of traditional financial institutions and non-financial institutions. Traditional financial institutions are mainly the Internet innovation of traditional financial business and electricity, such as innovation, non-financial institutions mainly refers to the use of Internet technologies

互联网金融风险管理文献综述

互联网金融风险管理文献综述 沈丽，林冬冬 (山东财经大学金融学院，山东济南250014) 摘要：互联网金融如雨后春笋般发展起来，2013年已然成为互联网金融井喷的一年。但是伴随互联网金融繁荣发展而来的风险更是不容小觑的，并引起了学者的广泛关注。在此背景下，将学者对于互联网金融的研究加以追踪并且进行综合评述，内容涉及互联网金融的涵义界定、互联网金融的风险识别以及互联网金融风险管理。通过综述学者的研究发现，目前对于互联网金融的研究尚不完善，对于互联网金融的界定还未统一以及在互联网金融风险管理方面如何权衡风险与收益也未得到应有的重视。因此，在互联网金融的研究方面尚有一定的发展空间，需要研究学者不断完善与充实。 关键词：互联网金融；风险；风险管理；余额宝 中图分类号：F830．9文献标识码：A文章编号：1008－2670（2014）05－0015－06 曾有人估计，互联网金融将吞噬传统金融，从而使后者成为“21世纪行将灭绝的恐龙”①。从四大行推出的网上银行，到淘宝联合天弘基金开发的余额宝，互联网金融衍生产品涉及的客户群和衍生产品的种类均保持快速增长。截至2013年12月31日，余额宝的规模已达1853．42亿元②，成为市场上规模最大的公墓基金。然而，火爆背后的风险不容小觑。2013年4月，上线未满月的P2P网贷企业“众贷网”宣布破产，随后“城乡贷”在其网站挂出歇业公告，两家网络平台相继倒闭。因此，互联网金融行业必须正视互联网金融的风险，及时全面制定互联网金融的风险管理策略。互联网金融的风险及风险管理问题引起了各方关注，本文将对学者们针对互联网金融的内涵界定、风险以及风险管理的研究脉络进行梳理。 一、互联网金融内涵的界定 关于互联网金融内涵的界定，学术界存在较大争议。以下是相关国内学者对互联网金融的界定研究成果，主要是从静态和动态两个角度界定互联网金融。 从静态角度看，分为产物说和模式说。产物说认为，互联网金融是现代互联网技术与金融相互结合的产物，是以网络等新技术手段为基础的一种金融创新形式。而且互联网金融的概念包括在网络的基础上对原有 收稿日期：2014－07－10 基金项目：山东省“金融产业优化与区域发展管理协同创新中心”规定性项目“区域金融风险生成机制与管控体系构建—以山东为例”。 作者简介：沈丽，女，山东莱州人，博士，山东财经大学金融学院教授，研究方向：货币银行学；林冬冬，女，山东临清人，山东财经大学金融学院硕士研究生，研究方向：货币银行学。 ①“21世纪行将灭绝的恐龙”来自于比尔·盖茨。十几年前，互联网刚出现时，比尔·盖茨预言如果传统银行不改变的话，传统银行终将灭绝。 ②数据来源于天弘基金2013年数据。

互联网金融外文文献翻译 2

外文出处：DeBonisR, Silvestrini A. Internet finance and its influence ontraditional banking [J]. Applied FinancialEconomics, 2016,3(5):409-425. 原文 Internetfinanceanditsinfluenceontraditionalbanking De BonisR, SilvestriniA A b stracts With the rapid development of information technology, Internet financialmodel graduallyrise.ThispapersummarizestheInternetfinancialmodelonthebasisofth e concept, features and functions of Internet financial model in strategy,customer channels,financing, pricingand financial disintermediation of the impact of the tra di ti o nal c om mercial bank. T his paper a r g ue s t hat Inte rne t financ i al m ode l in the short term will not stand in the way of commercial bank's traditional business modelandprofit,butinthelongtermcommercialBanksshoulduseoftheInternetfinancial model, in order to obtain the new development. At the same time, the sustainedandhealthy development of the Internet industry to rely on Internet financialenterprises e lf-di s c i pli ne,posit i ve i nnovat i on,but a lsoattrac t m orec us t ome rs,strengt he nt h e construction ofsystemsecurity. Key words: Financial innovation; Internet financial; Financialdisintermediation At p r e s ent, m obi le payment, online ba nking, m obil e ba nki ng and financ i al businessinChina'sbooming financialinnovationssuchascloud,thusformedanew kind of financial model -- the Internet finance. Big data era and brand creation,spread tothedevelopmentoffinancialinstitutionsisbothachallengeandopportunity.Alo ng with the development of the Internet financial, emerging Internet traditionalfinancialcompanies and financial institutions will be a fierce competition, the future mayeven change thetraditional financial management mode and operationpattern. The Inter net financial concepts, features andfunctions The concept of the Inter net financial. After years of development, Internet companies stay in business does not provide

互联网金融文献综述

《创意众筹的异质性融资激励与自反馈机制设计研究》黄玲，周勤 随着信息技术和科技手段的发展，一些看似不可思议的创意转化成产品，如打印机、可穿戴智能设备等，不仅改变着我们的日常生活，也促进了一批创意型企业的成长。同时，一些传统产业中的企业通过发展创意项目顺利摆脱了结构转型的困境。支持创意项目的发展有利于技术的创新和企业的转型。然而创意项目发展初期却面临诸多难题，其中融资难是其关键。创意项目的发展有利于技术的创新和企业的转型，而融资难是其发展初期面临的关键问题。而众筹为这一问题的解决提供了全新思路。本文从融资人、投资人和平台三方面说明了众筹模式三方目标各异，激励具有异质性。并建立模型，对这三方的激励模式进行了分析。之后，本文指出在众筹发展的初期，众筹机制的设计任务主要由平台来承担，肩负着教育市场和指导众筹的使命其目的是使更多的创意项目和资金有效对接，从而满足三方利益的一致诉求。随后，本文结合激励模式、众筹机制两方面进行了实证分析，实证表明，在阈值机制约束下，资金需求较小的创意项目倾向于选择众筹模式，融资方通过平台展示，向投资人发送项目质量信号，投资人在自我尽职审查机制下选择符合自身偏好的优质项目进行投资。借助“点名时间”数据研究表明，有效的质量信号在满足投资人偏好类型条件下能诱发投资激励，并通过众筹社区反馈渠道迅速传播，推动创意项目取得成功，反之则可能遭遇失败，这可以形成有效的众筹自反馈效应。最后，本文针对实证结果，提出了以改善异质性融资激励缺陷与创造自反馈机制的优良环境为基础的一系列政策建议。 《美国SEC众筹新规及其监管启示》龚映清，蓝海平 众筹作为一种科技融资的新模式，近年来在全球范围内发展迅速。众筹通过互联网有效匹配了资金供给与需求，为初创企业提供了一种全新且低成本的融资方式，是传统风险投资的一种重要补充。然而作为一种新模式，众筹模式也存在着包括股权众筹受限、投资者保护难等许多问题。因此，2013年10月，美国SEC正式发布了一项针对众筹的监管提案，以规范早期的众筹市场，促进其良性发展。该法案的主要内容包括：允许股权众筹、对发行人的要求以及对中介机构的要求。该法案的颁布对众筹模式产生了重大影响：一是放宽了投资者门槛，二是有利于化解中小企业融资难的问题，三是加速了众筹中介机构的发展。同时这一法案也存在一些不足，比如可能会增加发行企业成本，不利于投资者保护，增加监管成本等。但总的来说，美国众筹新规提案为众筹发展框定了边界，提供了土壤。本文针对中国众筹模式发展的中出现的政策监管不明、创新环境弱、投资者保护难度大、社会公众对众筹认识不足等问题，结合美国经验得出一些启示：一是要加快立法，二是要不断探索股权众筹方向及其可能路径，三是必须加大对投资者的保护，四是要加快信用体系建设。 《众筹项目的社交网络影响力预测与分析》杨扬，Ｃｈｕｎ－ＴａＬＵ，王菲菲，许进，ＰｈｉｌｉｐＳ．ＹＵ 与传统的募资方式相比，众筹具有募资速度快和成本低等优势，因而逐渐成为一种重要的募资渠道。然而，由于众筹平台中的大多数项目都以失败告终，众筹项目发起人、投资者和众筹平台的运营者都迫切地需要找到能够有效提高项目成功率的方法。本文针对众筹项目由于社会影响力不足而成功率较低的问题，提出了面向众筹平台的社交网络影响力预测方法。根据已有的数据，本文构造了一些特征作为研究对象，包括了项目统计特征、内容特征、社会行为特征和社会结构特征等４类累积特征。其后，结合ＫｉｃｋＳｔａｒｔｅｒ众筹平台数据和Ｔｗｉｔｔｅｒ社交网络中的相应数据外构建了模型，对众筹模式的影响力增长以及上述四项累积特征的重要性进行分析。实验结果表明：在整个推广过程中，众筹项目的社交网