The Misregulation of Person-to-Person Lending

Verstein, Andrew

doi:10.2139/ssrn.1823763

Cited by 26 publications

(26 citation statements)

References 8 publications

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“…If the current development instead leads to excess supply of P2P loans for securitization purposes, then questions concerning a potential weakening of lending standards in P2P lending may be justified. This finding is particularly severe because it exacerbates the general finding of the subprime crisis that securitization already led to lower lending standards (see, e.g., Verstein 2012 A crunch in a particular P2P loan segment might shake the confidence in platforms being specialized on this kind of loan, thus shaking loan generation and eroding earnings of those platforms as outlined in our subsection on platform risk. A subsequent collapse of a few platforms in this segment might shake overall confidence in P2P lending, with the potential for further platforms to collapse.…”

Section: Institutionalization Of P2p Marketsmentioning

confidence: 90%

“…Verstein (2012) impressively illustrates how the US model of P2P regulation in fact increases risk for lenders. In the US, P2P platforms are seen as issuers of securities, with the Securities and Exchange Commission (SEC) being responsible for their regulation.…”

Section: Risk Of Platform Defaultmentioning

confidence: 92%

“…As a consequence, lenders not only have to evaluate the creditworthiness of the borrower but that of the platform as well. Verstein (2012) argues that investors will lose money if either the borrower or the platform default, a problem that is ignored by most P2P lenders in the US and makes P2P lending in fact more risky than traditional banking. 30 Indeed, even Prosper acknowledges the chance of a platform default in its prospectus, although arrangements have been made to minimize this danger.…”

Section: Risk Of Platform Defaultmentioning

confidence: 99%

“…On the other hand, even without taking any credit risk, the platform still has to cover operational expenses. As mentioned above, different kinds of fees are charged, with up-front fees for 30 Verstein (2012) also emphasizes that this risk-shift from borrowers to platforms contradicts the idea of cutting out banks as middlemen. 31 See footnote 1.…”

Section: Risk Of Platform Defaultmentioning

confidence: 99%

“…Seen on the whole, there are advantages and disadvantages from the current liquidity structure of P2P lending. A considerable benefit is the fact that the absence of maturity transformation prevents the danger of run risks (Verstein 2012). With maturity transformation representing a significant driver of financial instability, the absence of this feature makes P2P lending initially less risky than traditional banking.…”

Section: Liquidity Risk and Secondary Marketsmentioning

confidence: 99%

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Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

Käfer

2017

Review of Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe aim of this paper is to discuss P2P lending, a subcategory of crowdfunding, from a (financial stability) risk perspective. The discussion focuses on a number of dimensions such as the role of soft information, herding, platform default risk, liquidity risk, and the institutionalization of P2P markets. Overall, we conclude that P2P lending is more risky than traditional banking. However, it is important to recognize that a constant conclusion would be misleading. P2P platforms have evolved and changed their appearance markedly over time, which implies that although our final conclusion of increased riskiness through P2P markets remains valid over time, it is based on different arguments at different points in time. In addition, we discuss that acting on P2P online platforms satisfies most possible definitions of shadow banking and shows significant similarities with many observed aspects of shadow banking. We thus infer that P2P lending can be considered part of the shadow banking sector. JEL Classification:F34, G21, G23

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Section: Institutionalization Of P2p Marketsmentioning

confidence: 90%

Section: Risk Of Platform Defaultmentioning

confidence: 92%

Section: Risk Of Platform Defaultmentioning

confidence: 99%

Section: Risk Of Platform Defaultmentioning

confidence: 99%

Section: Liquidity Risk and Secondary Marketsmentioning

confidence: 99%

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Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

Käfer

2017

Review of Economics

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Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Byanjankar

Mezei

Heikkilä

2021

Intell Sys Acc Fin Mgmt

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In recent years, peer‐to‐peer (P2P) lending has been gaining popularity amongst borrowers and individual investors. This can mainly be attributed to the easy and quick access to loans and the higher possible returns. However, the risk involved in these investments is considerable, and for most investors, being nonprofessionals, this increases the complexity and the importance of investment decisions. In this study, we focus on generating optimal investment decisions to lenders for selecting loans. We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk. In the process, we use internal rate of return as the measure of return. As the starting point of the model, we use machine‐learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data. Afterwards, we calculate the distance between loans using (i) default probabilities and, as a novel step, (ii) expected value. In the calculations, we utilize kernel functions to obtain similarity weights of loans as the input of the optimization models. Two optimization models are tested and compared on data from the popular P2P platform Lending Club. The results show that using the expected‐value framework yields higher return.

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The Legal Regulation of U.S. Crowdfunding: An Organically Evolving Patchwork

Heminway

2021

Legal Aspects of Crowdfunding

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The Misregulation of Person-to-Person Lending

Cited by 26 publications

References 8 publications

Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

The Legal Regulation of U.S. Crowdfunding: An Organically Evolving Patchwork

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