Estimating probability of default via delinquencies? Evidence from European P2P lending market

Article

Nigmonov, Asror, Shams, Syed and Urbonas, Povilas. 2024. "Estimating probability of default via delinquencies? Evidence from European P2P lending market." Global Finance Journal. 63. https://doi.org/10.1016/j.gfj.2024.101050
Article Title

Estimating probability of default via delinquencies? Evidence from European P2P lending market

ERA Journal ID18372
Article CategoryArticle
AuthorsNigmonov, Asror, Shams, Syed and Urbonas, Povilas
Journal TitleGlobal Finance Journal
Journal Citation63
Article Number101050
Number of Pages27
Year2024
PublisherElsevier
Place of PublicationNetherlands
ISSN1044-0283
1873-5665
Digital Object Identifier (DOI)https://doi.org/10.1016/j.gfj.2024.101050
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S1044028324001224
Abstract

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

KeywordsPeer-to-peer lending; LASSO method; Panel data; Marketplace lending; Default; FinTech
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020350299. Banking, finance and investment not elsewhere classified
Byline AffiliationsUniversity of New South Wales
University of Southern Queensland
Startup Wise Guys, Lithuania
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