P2P Lending Market Research Articles

Peer-to-peer lending in Vietnam and experience from other countries

Peer-to-peer (P2P) lending is gaining popularity in Vietnam, especially in the past two years. In fact, this form of lending appeared a long time ago in the world with the establishment of Zopa in the U.K. in 2005. There is neither much research nor a strict legal framework on P2P lending in Vietnam. As a result, P2P lending platforms encounter many problems, especially legal issues during their operation in Vietnam. The article aims to provide a theoretical framework on P2P lending and the practical situation in developed markets such as the U.S. and European. We also present an overview of the P2P lending market and the performance of P2P lending platforms in Vietnam which we find not operating up to their essence. Based on the analysis and experience from other countries, we suggest some solutions to increase the effectiveness of this form of lending in Vietnam’s market.

Social heterogeneity and local bias in peer-to-peer lending – evidence from China

This paper investigates the presence of local bias in the peer-to-peer (P2P) lending market and explores the social heterogeneous factors that may affect the formulation of the investor's local bias. We find that local biases are commonly present among investors. Investors have a 9.3% higher probability and put 105% more money in lending to local borrowers. We also find that overinvesting in local loans is correlated with higher default risk, lower recovery rate, and lower realized return, suggesting the underperformance of these locally biased investors. By taking advantage of the diverse local culture and institutional features in China, we further show that social heterogeneity, including geography, language, and social trust, affects the degree of local biases in the P2P lending market. We propose two debiasing techniques from the P2P platforms’ perspective.

Investor Sentiment and the Return Rate of P2P Lending Platform

This paper first constructs investor sentiment indices for the P2P lending market. Furthermore, the platform is divided based on five categories according to the P2P lending platform characteristics, such as the background of the platform and different transparency. We adopt a vector autoregressive to study the influence of investor sentiment on the return rate of different type of P2P platforms. The result shows that the platforms with lower transparency, higher return and the background of private are changed in the same direction with the sentiment. Therefore, as an external variable, investor sentiment can predict the stability of P2P lending platforms.

A consortium blockchain paradigm on hyperledger-based peer-to-peer lending system

This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer (P2P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyper-ledger-based Peer-to-Peer Lending System (HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.

Social Heterogeneity Influences on Local Bias --- Evidence from Peer-to-Peer Lending

This paper investigates the presence of local bias in the peer-to-peer (P2P) lending market and explores the social heterogeneous factors that may affect the formulation of the investor’s local bias. We find that local biases are commonly present among investors. Investors have a 9.3% higher probability and put 105% more money in lending to local borrowers. We also find that overinvesting in local loans is correlated with a higher default risk, lower recovery rate, and lower realized return, suggesting the underperformance of these locally biased investors. By taking advantage of the diverse local culture and institutional features in China, we further show that social heterogeneity, including geography, language, and social trust, affects the degree of local biases in the P2P lending market. We propose two debiasing techniques from the P2P platforms’ perspective.

Research on Investment Model of Internet Financial Loan Platform

In recent years, Peer-to-peer(P2P) lending platforms are in a stage of rapid development in China, which gradually become one of the important investment options for investors. A key challenge for personal investors in P2P lending market is how to get the expected benefit under certain risks. In this paper, we present the efficient frontier for investors with the combination theory of random forest, nuclear regression and Markowitz portfolio theory. The final experimental result reveals that the frontier can effectively provide investors more investment options.

The importance of detailed patterns of herding behaviour in a P2P lending market

ABSTRACTPrevious studies have provided contradictory evidence as to whether investors’ herding behaviour is rational or not in P2P lending markets. In this study, a path analysis model is adopted to identify a logical leap with regard to judgements of the rationality of investors’ herding behaviour per se. Empirical results show that the indicators of herding behaviour used in previous studies (i.e., number of lenders and relative time span for funding) are conflictingly affected by other attributes such as the borrower’s credit score and loan amount. This means that the rationality of investors’ herding behaviour is not deterministic but changes with the investors’ credit assessment style in each market. In addition, this study finds that investors’ accelerating bidding participation over time is positively related to loan repayment results through unidirectional pathways from the borrower’s credit score and loan duration, implying that investors’ bidding patterns can have predictive power concerning loan performance outcomes.

Business model of creating digital platform for tokenization of assets on financial markets

The article is devoted to the characterization of a business model for creating a digital platform for tokenization of assets on financial markets. The areas of application of tokenization of assets for financial market participants are considered. The legal framework is given, and the financial model using tokenization of the credit portfolio of a commercial bank is described. A method for calculating the interest rate of a package of tokens composed of tokens of various credits as well as an algorithm for calculating the size of a token for a loan with given PD and LGD parameters is given. It is shown that resulting from application of the proposed financial model, the role of banks in the financial market is changing. The bank emerges as the organizer of the P2P lending market from a traditional credit institution. As a result of this transformation the P2P market receives an additional impetus for development, and the bank increases its level of profitability. A comparison of the levels of profitability, liquidity and reliability of the proposed tools with the traditional instruments for attracting and placing funds is made.

The Bilateral Effects of Platform-Sponsored Collateral in Peer-To-Peer (P2P) Lending: Evidence from China

ABSTRACTUsing a unique large dataset collected from one of the oldest and largest P2P lending platforms in China, namely PPDAI, this article tests the bilateral effects of a special type of platform-sponsored collateral on lenders and borrowers. Both theoretically and empirically, we find that smart lenders disaggregate the surety and riskiness-signaling effects of such collateral. Our evidence indicates that lenders in China’s P2P market are smart: they anticipate that platform-collateralized loans are riskier than the non-collateralized ones in the second or higher rounds of fundraising. For the borrowers, platform-sponsored collateral increases the overall borrowing capacity owing to its surety effect. Therefore, combining the two aspects, platform-sponsored collateral increases the overall efficiency of the P2P lending market given the safety of the platform per se.

Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.

Platform Choice in Online Peer-to-Peer Lending Markets: A Joint Structural Model

Online peer-to-peer (P2P) lending is a two-sided market that enables direct interactions between borrowers and investors. The network effects that arise in P2P lending markets can make the decision-making process of both participant groups interdependent. However, extant research on P2P lending primarily focuses on decisions of one specific participant group without considering the interactive behaviors from the other side of the market. We attempt to fill this gap by developing a utility-based structural model that simultaneously governs both borrowers’ and investors’ platform choice decisions. Our results show that a platform’s short-term liquidity, cross-network effect (CNE), and direct-network effect (DNE) are the top three factors that positively drive investors’ platform choice. In contrast, a platform’s background, tenure, membership fee, long-term debt risk, and the average loan duration have a negative effect on their participation decision. On the borrower side, we find that a platform’s short-term liquidity, tenure, CNE, and DNE are the top four factors that attract their platform selection, while a platform’s background and membership fee will reduce their utility of choosing the platform. Overall, borrowers play a more important role than investors in the growth of a P2P lending platform. The counterfactual analyses suggest that a handful of interventions can be implemented to influence both investors’ and borrowers’ platform choice. The new information-transparency regulations issued in China are estimated to save investors more than $1.36 million. Our findings offer important managerial implications for platform managers and policy makers in the P2P lending market.

Modelling user pictures with hierarchical Dirichlet process of P2P lending market

The emergence of peer-to-peer (P2P) lending has drawn a lot of attention. The enormous data generated from this billions level market bring us a lots of challenges and opportunities. One interesting question of modelling this data is that can we discover the hidden pattern of users' characteristics from it? Currently, few works have been made to this area. In this article, we try to build a Bayesian probabilistic model to discover the latent user pictures. Especially, we build a user picture model via hierarchical Dirichlet process from the data of one of the biggest market, lending club. The discovered user picture is interpretable and can be evaluated from many perspectives. To demonstrate the usage of user picture, we also proposed a method to predict the loan status. The experimental results show our approach outperformed the comparison methods.

Complements Rather Than Substitutes: An Empirical Examination of Cryptocurrency and Online Peer-to-Peer Lending Markets

The authors develop a conceptual framework and derive testable predictions regarding effects of cryptocurrency markets on borrowers in peer-to-peer (P2P) lending markets from January 2017 to February 2019. Results indicate that the growth in cryptocurrency markets is associated with increased loan requests in the P2P market, especially for borrowers with good credit ratings, more knowledge about the cryptocurrency market, and likely to borrow for investing purposes. The findings imply that the P2P lending market is potentially a funding source for investment in the cryptocurrency market. The growth in cryptocurrency markets is further associated with low default and interest rates. The study has unique and important implications for theory and practice regarding the appropriate management of crowdfunding platforms to sustain a new wave of financial technology.

Visual analysis of risks in peer-to-peer lending market

Online peer-to-peer (P2P) lending financial products have been developing rapidly in recent years. This investment method is designed for people free of high-rate debts. However, the lending and borrowing affairs between anonymities may potentially produce risks, including wash sale and money laundering. Apart from the well-documented research on the causal factors and economic influence of the P2P lending market, limited attention has been paid to the risk management of individual P2P lending platforms. This study presents a visual analysis method that detects and analyzes risks in P2P lending transactions. Moreover, we evaluate our approach on real-world P2P data sets and report our findings.

Network topology and systemic risk in Peer-to-Peer lending market

Peer-to-Peer lending market is presented as a complex network and its topology characteristics, such as the ratio of brokers’ connections, degree, ratio of available node pairs, betweenness centrality, network density, network efficiency and edges’ loading ability have been investigated in this paper. The main contribution of this paper is that modeling and analyzing the systemic risk in P2P lending market in the view point of complex network based on these topology characteristics. And the empirical research has been carried out by using the real data from one of the largest online platforms in China (www.renrendai.com). Result shows that the relationship between lending–borrowing network topology and the default loans is significant, which implies that these factors have strong relations to systemic risk in the network.

Strategic Information Transmission in Peer-to-Peer Lending Markets

Peer-to-peer (P2P) marketplaces, such as Uber, Airbnb, and Lending Club, have experienced massive growth in recent years. They now constitute a significant portion of the world's economy and provide opportunities for people to transact directly with one another. However, such growth also challenges participants to cope with information asymmetry about the quality of the offerings in the marketplace. By conducting an analysis of a P2P lending market, the authors propose and test a theory in which countersignaling provides a mechanism to attenuate information asymmetry about financial products (loans) offered on the platform. Data from a P2P lending website reveal significant, nonmonotonic relationships among the transmission of nonverifiable information, loan funding, and ex post loan quality, consistent with the proposed theory. The results provide insights for platform owners who seek to manage the level of information asymmetry in their P2P environments to create more balanced marketplaces, as well as for P2P participants interested in improving their ability to process information about the goods and services they seek to transact online.

Online P2P Lending and Regulatory Responses in China: Opportunities and Challenges

As a main form of the Fintech industry, China’s online P2P lending market has undergone a period of explosive growth in the past few years to become the largest in the world, with online lending platforms having mushroomed across the country. This is a consequence of the simultaneous emergence of three key factors, namely deep penetration of internet, large supply of funds and unmet financial needs. The market exhibits several distinctive features in terms of the size of platforms, the level of market concentration and business models. As online lending gathers moment in China, many problems have come into light. China has recently established a relatively complete regulatory regime for online lending, introducing a number of significant changes, such as the restriction on the business model that can be adopted by platforms, registration requirements, custodian requirements, information disclosure requirements and lending limits. The new regime will have far-reaching implications, including a reshuffling of the market and more collaboration between online lending platforms and traditional banks. A comparative analysis of the Chinese experience with those in other jurisdictions such as the US, the UK, Hong Kong and Japan is conducted to examine the extent to which the new regime is likely to achieve a proper balance between its two main objectives, namely facilitating the growth of the online lending market and protecting financial consumers. While the new regime is generally sound, its efficacy will ultimately depend on how it is enforced in practice.

Current Status and Future Prospects of China’s Peer-to-Peer Lending Regulation

China’s peer-to-peer lending industry is now at a critical moment that the Draft Measures on P2P lending regulation has just been put forward, determining the industry’s future direction of development. This research is going to analyze the current status of China’s P2P lending market and its regulation first. Then comparing China with the US and the UK in terms of P2P lending business models and regulation. Finally combining China’s own features and the US & UK’s experiences to find out the problems with China’s P2P lending regulation and provide corresponding recommendations to solve the problems.

Recent Development of FinTech Regulation in China: A Focus on the New Regulatory Regime for the P2P Lending (Loan-based Crowdfunding) Market

... With adequate profit, capital is very bold. A certain 10 per cent will ensure its employment anywhere; 20 per cent certain will produce eagerness; 50 per cent, positive audacity; 100 per cent will make it ready to trample on all human laws … – T. J. Dunning FinTech, or what in China is more commonly referred to as Internet Finance, is the alternative finance supplying a wide range of conventional or innovative financial instruments via online marketplace channels. These channels fall outside the traditional avenues of capital raising and financial intermediation.1 Its common characteristic is that ‘traditional financial institutions and Internet companies use Internet technology for: payments, internet lending, public equity financing, internet fund markets, internet insurance, internet trust, consumer finance’.2 In recent years, FinTech innovation in China has witnessed an unprecedented boom, which has transformed China into one of the world’s largest online alternative finance markets by transaction volume.3

Herding in a P2P lending market: Rational inference OR irrational trust?

Herding exists in stock markets worldwide. However, little research has tested whether lenders in P2P lending markets herd. This study uses data for 4,856,413 bids placed on 120,207 loan requests in Renrendai from the website’s inception in October 2010 through January 2015 to examine the dynamic relationship between prior cumulative bids and current bids. We find that lenders appear to imitate each other’s behavior and herd in the P2P lending market funding percentage and time-limit effects are controlled for. We also examine the classification of herding in the P2P lending market by dividing the bids into manual and automated bids. We find evidence for the existence of both rational and irrational herding in the P2P lending market.