Female Small Business Owners in China: discouraged, not discriminated
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Using a unique small business loan application data from a prominent peer-to-peer (P2P) loan platform in China, we investigate whether female-owners are discriminated when applicants experience consecutive failed loan requests. We provide evidence that lenders do not gender discriminate, yet female business owners are discouraged from applying for funds after a failed earlier attempt. Financially literate owners are not likely to reapply following a failed application while riskier applicants keep requesting for funds by offering higher interest rates. Lenders fund less risky business owners with high credit ratings and high income, but not those who offer high interest payments.
What does not kill us makes us stronger: The story of repetitive consumer loan applications
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We investigate borrower and lender behaviours when the borrower has experienced a sequence of failed loan applications. Our analysis is based on half a million observations from an established peer-to-peer (P2P) loan platform in China over 2010-2018. We find that borrowers with better credit scores and those who can offer higher rates are likely to reapply for funds after a failed attempt. Yet, females and applicants with better education are discouraged quickly. On the funding supply side, lenders strive to fund safe borrowers with high credit rating and high income, not those who offer high interest rate.
Asset mispricing in loan secondary market
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This study documents the degree of mispricing in loan secondary market. Using data from Bondora, a leading European peer-to-peer lending platform, over the 2016-2019 period, we find evidence for the existence of mispricing: poor quality assets are successfully sold while good assets are not sold. We argue that mispricing is mainly driven by the differences in market participants’ perceptions about asset values. Once sellers learn about the belief dispersion, they revalue their assets according to buyers’ perception and exploit the mismatch in the subsequent listings.
Herding Behaviour in P2P Lending Markets
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This paper investigates herding behaviour in peer-to-peer (P2P) Lending markets. Our data come from Renrendai.com, a popular Chinese P2P platform, which have about 110,000 bidding over 2010-2018. Our investigation yields evidence of herding behaviour, whose impact varies depending on the time of day. Further scrutiny provides evidence in favour of rational herding. We next implement an innovative approach and examine data on individual bidder activity to account for investor heterogeneity. This new approach provides absolute evidence of herding behaviour, as herding is a behavioural response of investors under uncertainty.
What does not kill us makes us stronger: the story of repetitive loan rejections
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We examine factors that affect borrowing and lending when borrowers have experienced one or more failed funding applications. Using a unique dataset of 1,2 million loan applications from a peer-to-peer (P2P) loan platform in China, we investigate whether decision to apply varies with respect to the number of previous consecutive failures. Our results suggest that borrowers with better education are not likely to apply after a failed attempt. Repeat applicants provide less information in their loan narratives and are likely to pay higher interest rates per loan. We also find that females are discouraged early on. In relation to lender behavior, similar preferences apply across those who had one or more previous rejections as they prefer applications with high credit ratings and high income. Finally, our results are generally similar for both consumer and business loans.
Role of Verification in Peer-to-Peer Lending
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Using data from a leading Chinese Peer-to-Peer (P2P) lending platform from 2012 to 2015, we investigate the role of verification in the P2P lending market. We find that borrowers with thorough and complete verification are more likely to obtain funding and also less likely to default on loans. We also find that borrowers that have incomplete verification are more likely to upwardly misrepresent their income. This leads to higher default rates for this group when compared to the default rates of more thoroughly verified borrowers. The further analysis documents that returning borrowers are more likely to maintain a good credit record. We discuss the implications of our findings for the role of verification in the growing P2P lending sector and the design of a stable financial system.