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Small Business Borrowing And Peer‐To‐Peer Lending: Evidence From Lending Club

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  • Adam Nowak
  • Amanda Ross
  • Christopher Yencha

Abstract

We investigate the ability of small business borrowers to signal to investors their credit worthiness through the use of text descriptions in the peer‐to‐peer lending market. Specifically, we examine the relationship between the loan description written by a borrower and whether or not the project is funded by investors. Using textual analysis, we find that small business loan descriptions can be used to predict the likelihood that the loan will be funded. We also find that an index, created from a textual analysis of the words used in the loan description, can forecast the performance of the loan, specifically whether or not the loan defaults. This index has the strongest impact when we focus on borrowers with low FICO scores, suggesting that for these individuals the description can signal information that standard measures used for lending purposes cannot. Overall, it appears as though investors are making investment decisions based on proper and relevant signals given by the borrowers through the loan description. (JEL D47, D53, D82, D83, G14, G21)

Suggested Citation

  • Adam Nowak & Amanda Ross & Christopher Yencha, 2018. "Small Business Borrowing And Peer‐To‐Peer Lending: Evidence From Lending Club," Contemporary Economic Policy, Western Economic Association International, vol. 36(2), pages 318-336, April.
  • Handle: RePEc:bla:coecpo:v:36:y:2018:i:2:p:318-336
    DOI: 10.1111/coep.12252
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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

    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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