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Borrower’s default and self-disclosure of social media information in P2P lending

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  • Ruyi Ge

    (Shanghai Business School)

  • Juan Feng

    (City University of Hong Kong)

  • Bin Gu

    (Arizona State University)

Abstract

Background We examine the signaling effect of borrowers’ social media behavior, especially self-disclosure behavior, on the default probability of money borrowers on a peer-to-peer (P2P) lending site. Method We use a unique dataset that combines loan data from a large P2P lending site with the borrower’s social media presence data from a popular social media site. Results Through a natural experiment enabled by an instrument variable, we identify two forms of social media information that act as signals of borrowers’ creditworthiness: (1) borrowers’ choice to self-disclose their social media account to the P2P lending site, and (2) borrowers’ social media behavior, such as their social network scope and social media engagement. Conclusion This study offers new insights for screening borrowers in P2P lending and a novel usage of social media information.

Suggested Citation

  • Ruyi Ge & Juan Feng & Bin Gu, 2016. "Borrower’s default and self-disclosure of social media information in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-6, December.
    • Handle: RePEc:spr:fininn:v:2:y:2016:i:1:d:10.1186_s40854-016-0048-3
    DOI: 10.1186/s40854-016-0048-3
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    References listed on IDEAS

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    2. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    3. Nadia Nahar Purkayastha & Şule Erdem Tuzlukaya, 2020. "Determination Of The Benefits And Risks Of Peer-To-Peer (P2p) Lending: A Social Network Teory Approach," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 9(3), pages 131-143.
    4. Yanhong Guo & Shuai Jiang & Wenjun Zhou & Chunyu Luo & Hui Xiong, 2021. "A predictive indicator using lender composition for loan evaluation in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.

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