Investments in p2p on the Internet. Peer-to-peer lending

Figures Abstract Peer-to-peer P2P lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions.

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In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans.

To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper.

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By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions.

Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models.

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  2. United Kingdom[ edit ] Zopafounded in Februarywas the first peer-to-peer lending company in the United Kingdom.
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  5. A Bayesian Investment Model for Online P2P Lending | SpringerLink
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The experimental results of investments in p2p on the Internet hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other.

We conclude that the hybrid model i. This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this study are third-party data i. Programs Foundation of Ministry of Education of China Competing interests: The authors have declared that no competing interests exist. Introduction Peer-to-peer P2P lending is an emerging financial market.

Peer-to-peer lending lets you be the borrower or the investor

In recent years, more people are engaged in this financial platform. For example, the volume binary options tatunashvili business and the turnover of Prosper and Lending Club, the large-scale online P2P lending intermediary agents in the United States, are 50 million and nearly million per month, respectively.

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The P2P lending is developing rapidly all over the world. In Prosper, individuals either request to borrow money, take a borrower role, or buy loans as a lender.

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The borrower sets the amount of money he or she needs and the maximum rate he or she would be willing to pay for this loan by posting a listing. Each lender will scan these loans to bid a partial amount and give the minimum rate they are willing to receive.

Average origination fee is 4. There is no prepayment penalty. Your credit history, credit score, and other details will be evaluated to predict your risk when you apply.

The main difference between P2P lending and traditional bank industry is that in the former, each lender can not only obtain the loan's financial information, but also evaluate the risk of bidding according to the borrower's social characteristic. On the other hand, Prosper, as investments in p2p on the Internet intermediary agent of P2P lending, collects many borrowers and lenders, help users borrow money quickly or gain benefits by investing.

In this Internet platform, investors and borrowers form an M-to-N relation model called bipartite graph, in which a lender can invest N loans, and a loan may be accepted by M investors. P2P lending, as a burgeoning financial market, becomes a new field for academic research.

In recent years, the social networking services on P2P lending have been explored extensively.

A decision support model for investment on P2P lending platform

Berger and Gleisner [ 1 ] found that these market participants act as financial intermediary and can significantly improve borrowers' credit conditions by reducing information asymmetries, predominantly for borrowers with less attractive risk characteristics. Freedman and Jin [ 2 ] examined what information problems exist on Investments in p2p on the Internet and whether social networks can help alleviate the information problems.

They found that the estimated returns of groups loans are significantly lower than those of non-group loans partially due to lender learning and partially due to Prosper eliminating group leader rewards. Lin et al. The social networks approaches were also applied in other fields, such as bioinformatics [ 4 ]. Collier and Hampshire [ 5 ] draw on the theory from the Principle-Agent perspective to empirically examine the signals that enhance community reputation.

Sergio [ 6 ] measured the influence of social interactions in the risk evaluation of a money request; with a special focus on the impact of one-to-one and one-to-many relationships.

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His results showed that fostering social features increases the chances of getting a loan fully funded, when financial features are not sufficient to construct a differentiating successful credit request. Chen et al. Some researchers take the perspective of borrowers when developing a model. Wu and Xu [ 8 ] proposed a decision support system based on intelligent agents in P2P lending for borrowers. The system provides borrowers with individual risk assessment, eligible lender search, lending combination and loan recommendation.

The empirical results in [ 9 ] displayed that borrower' decisions, such as loan amount and interest rate, will determine whether he or she could successfully find loans or not. Herzenstein et al. To help investors make better investment decision, Luo et al.

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Katherine and Herrero-Lopez [ 13 ] examined the behavior of lender in a large peer-to-peer lending network and find that, while there exists high variance in risk-taking between individuals, many transactions represent sub-optimal decisions on the part of lenders. Lauri et al.

Singh et al. They found that within each credit grade, there exist subgroups which give positive return.

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  • Peer-to-peer lending - Wikipedia

For these subgroups, risk is aligned with return. In addition, the groups of loans with lower credit grades are more efficient in terms of risk and return alignment than those with higher credit grades. Klafft [ 16 ] demonstrated that following some simple investment making money on the internet success code may improve profitability of a portfolio and lead to acceptable returns for all credit rating categories with an exception of the high-risk ones.

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Garman et al. Iyer et al.

They examined this capability using a methodology that takes advantage of the category of the borrowers and found that lenders are able to use available information to infer creditworthiness that is captured by a borrower's credit score. P2P lending involves diverse elements, which renders research opportunities and challenges that are under-explored currently. The goal of this paper is to predict new investment abilities of investors and filter good requests in new loans on the basis of the bipartite graph model.

This work focuses on synthesizing those old manifestations of investors in the past investments and providing a comprehensive evaluation. The performance of old investors' will help discover and analyze good new loans and reliable new investments according to the lending and investing model generated from P2P lending data. The organization of this paper is as follows: Section 2 defines a M-to-N relation model called bipartite graph, which is based on the relation of investors and loans in P2P lending.

My Entire Investment Portfolio (It's Not Just P2P Lending)

Section 3 quantitatively analyzes the comprehensive evaluation of new investors and unknown status loans by modeling an iteration computation model.

Section 4 gives an integrated decision model to help investors pick trustworthy loans. Section 5 validates the effectiveness of the proposed investment decision model on the basis of real world data from the Prosper platform. By comparing current experimental results with those obtained by BayesNet [ 19 ], Logistic [ 20 ], and Average, we show that our computation model can help investors make better investment decisions. In addition, our empirical results demonstrate that the logistic classification model and the proposed iteration computation model complement each other.

In this marketplace, borrowers submit requests for loans, and lenders make bids on them.

As a result, the hybrid model i. Finally, Section 6 presents conclusions of this work. Comprehensive evaluation of investor and load Investing network P2P lending on Prosper is similar to that in the stock market, in which each investor can disperse his or her money to numerous loans and relatively one loan may be allocated by a multi-lender. This many-to-many relationship can be modeled as a bipartite graph which is widely used to model the relationship between two types of entities.

We will use the bipartite graph to explain the relationship of investors and investees. In the bipartite graph of P2P lending relationship, investors and borrowers are different types of entities, and the weight of edges is the amount of investors bidding for loan. In particular, on the Prosper online platform, if one borrower needs to borrow a sum of money, he or she must apply for a loan.

After the application is approved by Prosper, a listing will be posted for all investors. Each lender who scans the loan can bid a partial amount until completed in full. Fig 1 shows an example of this relation.

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