The Role of Social Capital in People-to-People Lending Marketplaces

The objective of this paper is to investigate the role of social capital in for-profit People-to-People (P2P) lending marketplaces such as Prosper, the largest P2P lending marketplace in the US. We examine whether marketplace members (lenders, borrowers) are able to capitalize on borrowers\u27 accumulated social capital. From a borrower\u27s perspective, we investigate the influence of social capital on borrowers\u27 chances to obtain funding and better interest rates in general as well as by borrower groups and over time. From a lender\u27s perspective, we investigate the influence of borrowers\u27 social capital on loan payment. We use data over a time span of two and a half years from Prosper, and analyze more than 200,000 loan requests and 27,500 loans. Our results suggest that social capital does not provide equal benefits to all members of Prosper and that mechanisms to promote social capital should be carefully designed

Herding in Multi-winner Auctions

Herding behavior is widely observed in auctions. There are rational reasons for herding but herding can also be counterproductive. We found evidence of herding behavior and sub-optimal outcome in a multi-winner auction setting. This study adds to the knowledge of herding by looking at herding in an auction setting where there is extra incentive to herd (multi-winner auction). Our findings reconfirm evidence in previous research about strategic usage of herding that diminishes after certain threshold; in addition, our findings indicate sub-optimal outcomes of herding behavior which include unjustified risk-return ratio, low ROI, wasted investment opportunities, and underutilized resource

A Model for Lender-Borrower Trust in Peer-To-Peer Lending

This research examined factors that influenced lender’s trust towards the borrower. The peerto- peer lending platform facilitated lending mechanism between lender and borrower. However, the loan was often considered as an unsecured loan, since there was a lack of traditional financial data. Using literature review, this research analyzed the determinant factor to establish trust between borrower and lender. Based on Elaboration Likelihood Model (ELM), the result of this research proposes a model for trust building between lender and borrower. The model categorizes information to establish trust into hard information, soft information, and social capital

Determinants and Consequences of Herding in P2P Lending Markets

In this paper, we are interested in the factors that influence herding behavior in P2P lending marketplaces. We are using data from Prosper.com to examine whether internal market specific factors and external economic factors influence the amount of herding exhibited in the market. We also investigate what consequences herding has and how marketplace participants can benefit or suffer from herding behavior in the marketplace. Based on previous models of herding in P2P lending, we calculate a herding measure over time. This herding measure is the basis for our analyses. Our preliminary analyses show support that internal factors measuring uncertainty, lenders experience, and search costs in the market influence herding. We receive inconclusive results for external factors measuring uncertainty, volatility, and bullishness in the marketplace’s economic environment. Herding has several implications for borrowers and lenders including potentially lower interest rates for borrowers but fewer completed listings

The effects of lender-borrower communication on P2P lending outcomes

Conference Theme: Exploring the Information FrontierLenders face great uncertainty because of the information asymmetry problem in peerto-peer (P2P) marketplaces. This paper studies an online feature on a P2P platform, which allows lenders to seek information directly from the borrower of a loan, and examines the impact of the direct lender-borrower communication on the funding outcomes and the loan performance. Our analysis results show that the number of lender comments is negatively associated with funding success. This implies that as a listing receives more comments from lenders, the chance of getting funded is lower. On the other hand, the number of borrower responses and response length are positively associated with funding success, although they cannot help reduce the final interest rate. The role of borrower response is even stronger for listings with poor credit grades. The loan performance (i.e., default ratio), however, cannot be predicted based on the amount of lender-borrower communication.postprin

The Roles of Social Capital in Online P2P Lending Markets Under Different Cultures: A Comparison of China and America

Online P2P (People-to-People or Peer-to-Peer) lending has very rapid development since it was appeared in 2005. In order to mitigate asymmetric information between borrowers and lenders, some online P2P market allows members building their social networks (such as Prosper, CommunityLend, PPDai etc). By empirical analyzing the transaction data of Prosper (largest P2P market in US) and PPDai (largest P2P market in China), the paper verifies that the social capital systems have a positive influence on borrower’s loan performance on the markets. However, on both markets, the loan interest rate mainly dependents on borrower’s hard information rather than their social capital. Furthermore, it concludes that borrower’ social network in PPDai is much more useful and effective than in Prosper by comparing the empirical results, which could be helpful for the credit system development of Chinese online P2P lending markets based on the conclusions

Kajian Faktor Yang Memengaruhi Adopsi Sistem Pijaman Peer To Peer Lending

Abstract. Medium, Small, Micro Entrprices (MSME) have a big role to play in the economy in Indonesia. Kemenkop UMKM (2017) states that as many as 80.9% of micro businesses in Indonesia do not yet have access to bank financing. One way to overcome barriers to MSME lending is through financial technology interventions through peer-to-peer lending (P2P lending) financing. The purpose of this study is to analyze the factors influencing MSME intention and behavior to use P2P lending services using the UTAUT2 analysis model (Venkantesh et al. 2012) with Structural Equational Modeling (SEM) analysis tools. The results of this study indicate that there are four variables that strongly and positively influence the intention to use P2P lending services among MSMEs, namely hedonic motivation, social influence, effort expectation, and performance expectancy. Then the results of this study also show that the behavior of using P2P lending services is strongly and positively influenced by gorenment regulation, risk perception, and behavioral intention.Keywords: MSME, P2P lending, Structural Equation Modeling (SEM), and UTAUT2Abstrak. UMKM memiliki peran yang besar terhadap perekonomian di Negara Indonesia. Kemenkop UMKM (2017) menyatakan bahwa sebanyak 80.9% usaha mikro di Indonesia belum mendapatkan akses pembiayaan perbankan. Salah satu cara untuk menyelesaikan hambatan penyaluran kredit UMKM adalah melalui intervensi teknologi finansial melalui pembiayaan `peer-to-peer lending (P2P lending). Tujuan dari penelitian ini adalah menganalisis faktor-faktor mempengaruhi niat dan perilaku UMKM untuk menggunakan layanan P2P lending dengan menggunakan model analisis UTAUT2 (Venkantesh et al. 2012) dengan alat analisis Structural Equational Modeling (SEM). Hasil dari penelitian ini menunjukkan bahwa terdapat empat variabel yang secara signifikan memengaruhi niat penggunaan layanan P2P lending dikalangan UMKM yaitu hedonic motivation, social influence, effort expectacy, dan performance expectancy. Kemudian hasil penelitian ini juga menunjukkan bahwa perilaku penggunaan layanan P2P lending dipengaruhi secara signifikan oleh gorenment regulation, risk perception, dan behavioral intention..Kata kunci: P2P lending, Structural Equational Modeling (SEM), UMKM, dan UTAUT

The role of lenders´ geographical diversification in P2P transactinos

In this dissertation,I provide novel evidenceof the impact of geographical diversification on loan interest rates. The findings, based on a unique dataset of all P2P transactions in a UK platformover the period 2010-2013, suggest that more geographically-diversified lenders are more likely to impose lower interest rates in their contract terms in P2P transactions, while more concentrated lenders (in terms of geographical concentration of the activities) practice higher loan interests rate. These results are robust to an alternative econometric approach

Network Effects In Two-Sided Electronic Market: A Cross-Country Empirical Analysis Of Online P2P Lending Market

With a two-sided model, this paper reports an empirical research investigating online Peer-to-Peer lending marketplaces, PPDai.com in China and Prosper.com in US. We observe that the platform’s profit-maximizing pricing strategies for the agents in the online P2P lending marketplaces are mainly related to the network effects between and within the two sides. Agents’ inter-group and intra-group network externalities depend on the demand-supply relationships, which is unlike the assumptions of negative intra-group network externalities and positive inter-group network externalities in the previous theoretical research of electronic commerce. Besides, as assumed in the theoretical model, it demonstrates significant negative price elasticity of demand and supply on both platforms. Based on the theoretical model and empirical results, we analyze the two platforms’ profit-maximizing pricing strategies, and explain the rationality and deficiency of the strategies. The findings enhance our understanding of the two-sided electronic market, which could shed light on how the platforms make price strategies in this kind of electronic market

Prosper—The eBay for Money in Lending 2.0

When a bank or a credit union turns you down for a loan because your debt-to-income ratio is too high, can you turn to total strangers to get the money? Yes, you can, and we are not talking about criminal acts. It\u27s called peer-to-peer (P2P) lending or social lending. Prosper was the first company to take the vision of social lending and convert it into practice on the Internet in the United States, and it enjoyed fast growth after launch. Four years later, however, it is facing old and new challenges, and its survival is on the line. This case depicts the opportunities and pressures Prosper faced, its actions and reactions, and its future. Prosper has made and will make many important decisions, and Prosper’s successes and challenges are rich material to study