Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market

Nigmonov, Asror; Shams, Syed; Alam, Khorshed

doi:10.1016/j.ribaf.2021.101516

Cited by 27 publications

(20 citation statements)

References 51 publications

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“…Style adapted from (Manz, 2019;Tabor et al, 2018) (Alaoui Mdaghri, 2022Armona et al, 2022;Boumparis et al, 2019;Carvalho et al, 2020;Castillo et al, 2018;Cowling et al, 2018;Croux et al, 2020;Dermine & de Carvalho, 2006;Fianto et al, 2019;Fidrmuc & Hainz, 2010;Gao et al, 2022;Goedecke, 2018;Hartarska & Gonzalez-Vega, 2006;Karadima & Louri, 2020;Kavussanos & Tsouknidis, 2016; Y. Y. Lee et al, 2020;Legendre et al, 2021;M & N, 2020;Mangrum, 2022;Martinez-Burgos et al, 2021;Mayock & Tzioumis, 2021;Mirpourian et al, 2016;Nigmonov et al, 2022;Odeh et al, 2011;Radivojević et al, 2019;Saengchote & Samphantharak, 2022;Sánchez Serrano, 2021;Trautmann & Vlahu, 2013;Zhang et al, 2020) Liquidity…”

Section: Findings and Discussionunclassified

“…The studies of the borrower-specific determinants of NPL are classified into Internal factors, ( (Cowling et al, 2018;Fianto et al, 2019;Fidrmuc & Hainz, 2010;Gao et al, 2022;Mayock & Tzioumis, 2021;Odeh et al, 2011;Radivojević et al, 2019;Zhang et al, 2020); Institutions(M & N, 2020), External environment (Goedecke, 2018;Nigmonov et al, 2022;Radivojević et al, 2019) and Social issues (Castillo et al, 2018;Hartarska & Gonzalez-Vega, 2006;Trautmann & Vlahu, 2013). Borrower organizational profile.…”

Section: Borrower Specific Determinantsmentioning

confidence: 99%

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Determinants of Non-Performing Loans Between Islamic And Conventional Banks: A Systematic Literature Review

Almuraikhi¹

2022

jeb

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This article conducts a systematic literature review of 52 papers on the determinants of non-performing loans (NPL) published for 2006-2022 in peer-reviewed journals. This paper adds to the development of this emerging field of systematic review in non-medical field settings by mapping existing research to inform future research exploits. This study synthesizes NPL studies by applying the systematic review methodology. The data synthesis is done through first and second-level codes. The first level codes group studies according to bank-specific, borrower-specific and comparative (Islamic vs conventional bank types). The literature is conscientiously analyzed through 13 subcategories using an open coding process. The literature synthesis shows that borrower-specific factors that may determine NPL include borrower profile, internal factors, external factors, and social issues. Also, the bank-specific factors that may influence NPL growth include economic conditions, management competence, profit maximization, and information economics. The findings highlight that the way borrower profiles, banks-specific factors and macroeconomic factors interact to influence NPL still needs more understanding. Further, Islamic banks and conventional banks seem to behave similarly regarding many bank-specific and macroeconomic factors even though the profit and loss sharing contract used in Islamic banks was deemed to moderate NPL positively. But the borrower-specific factors' interaction with bank-specific and macroeconomic factors of Islamic banks clients remains unclear. This highlights an important research gap in understanding the behavior of Islamic bank borrowers towards NPL prediction.

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Section: Findings and Discussionunclassified

Section: Borrower Specific Determinantsmentioning

confidence: 99%

Determinants of Non-Performing Loans Between Islamic And Conventional Banks: A Systematic Literature Review

Almuraikhi¹

2022

jeb

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“…2 We support the inclusion of economy-specific variables with existing literature on both traditional financial institutions (Ali and Daly 2010 ; Bofondi and Ropele 2011 ; Greenwald and Stiglitz 1993 ; Kou et al 2021 ; Stiglitz and Weiss 1992 ) and alternative financial markets (Dushnitsky et al 2016 ; Mollick 2014 ; Ning et al 2015 ; Wang et al 2021 ). The existing studies on marketplace lending extensively use borrower and loan characteristics in estimating loan defaults (Cai et al 2016 ; Galema 2020 ; Li et al 2018 ; Nigmonov et al 2021 ; Serrano-Cinca et al 2015 ; Wei and Lin 2017 ). Thus, we use the vector of borrower-specific control variables denoted as in Eq.…”

Section: Methodology and Datamentioning

confidence: 99%

COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market

Nigmonov

Shams

2021

Financ Innov

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As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market. Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period. COVID-19 pandemic has a significant impact on default risk during May and June of 2020. We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption. Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

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“…However, it is clear that P2P lending is quickly spreading globally. The P2P lending platform's primary role is to match the demand for and supply of funds (Nigmonov et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

Aliano

Alnabulsi²,

Cestari³

et al. 2023

ESJ

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The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending.

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Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market

Cited by 27 publications

References 51 publications

Determinants of Non-Performing Loans Between Islamic And Conventional Banks: A Systematic Literature Review

Determinants of Non-Performing Loans Between Islamic And Conventional Banks: A Systematic Literature Review

COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

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