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Small Business Borrowing and Peer-to-Peer Lending: Evidence from Lending Club

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  • Adam Nowak

    (West Virginia University, Department of Economics)

  • Amanda Ross

    (West Virginia University, Department of Economics)

  • Christopher Yencha

    (West Virginia University, Department of Economics)

Abstract

This study is interested in the ability of borrowers and lenders to signal to each other in the peer-to-peer lending market. We focus on small business loans and investigate the relationship between the loan description that a borrower provides and the impact of this description on the potential funding of the loan by investors. We find that the loan descriptions in the data can be used to predict the probability that the entire loan will be funded. In addition, we also find that an index created from a textual analysis of the loan description can be used to forecast the performance of the loan; a 1 standard deviation increase in the index will decrease the odds of default by 14%. Thus, it appears as if investors are not making investment decisions based on improper signals.

Suggested Citation

  • Adam Nowak & Amanda Ross & Christopher Yencha, 2015. "Small Business Borrowing and Peer-to-Peer Lending: Evidence from Lending Club," Working Papers 15-28, Department of Economics, West Virginia University.
    • Handle: RePEc:wvu:wpaper:15-28
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    File URL: https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=1158&context=econ_working-papers
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    Cited by:

    1. Michal Polena & Tobias Regner, 2018. "Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class," Games, MDPI, vol. 9(4), pages 1-17, October.
    2. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    3. Ly Nguyen & Mominul Ahsan & Julfikar Haider, 2024. "Reimagining Peer-to-Peer Lending Sustainability: Unveiling Predictive Insights with Innovative Machine Learning Approaches for Loan Default Anticipation," FinTech, MDPI, vol. 3(1), pages 1-32, March.
    4. Soumajyoti Sarkar & Hamidreza Alvari, 2020. "Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org," Papers 2006.12995, arXiv.org.
    5. Carlos Sanchis-Pedregosa & Emma Berenguer & Gema Albort-Morant & Jorge Anton Sanz, 2020. "Guaranteed Crowdlending Loans: A Tool for Entrepreneurial Finance Ecosystem Sustainability," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(55), pages 775-775, August.
    6. Yinghui Chen & Xiaolin Gong & Chien-Chi Chu & Yang Cao, 2018. "Access to the Internet and Access to Finance: Theory and Evidence," Sustainability, MDPI, vol. 10(7), pages 1-38, July.
    7. Pankaj Kumar Maskara & Emre Kuvvet & Gengxuan Chen, 2021. "The role of P2P platforms in enhancing financial inclusion in the United States: An analysis of peer‐to‐peer lending across the rural–urban divide," Financial Management, Financial Management Association International, vol. 50(3), pages 747-774, September.

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    More about this item

    Keywords

    small business borrowing; peer-to-peer lending;

    JEL classification:

    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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