IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v50y2019icp246-251.html
   My bibliography  Save this article

Too long to be true in the description? Evidence from a Peer-to-Peer platform in China

Author

Listed:
  • Li, Zhiyong
  • Zhang, Haiyang
  • Yu, Mei
  • Wang, Hairan

Abstract

This paper explores how loan descriptions provided by borrowers influence both the lenders’ and the platform’s behaviour in granting loans and setting interest rates. The empirical results show that textual length serves as a strong signal to identify the borrowers' quality. Strategic signalling theories cannot explain the borrowers' behaviour in China. We further show that the peer-to-peer platform is able to benefit from taking description length into consideration and that there exists room for investors to benefit from this information.

Suggested Citation

  • Li, Zhiyong & Zhang, Haiyang & Yu, Mei & Wang, Hairan, 2019. "Too long to be true in the description? Evidence from a Peer-to-Peer platform in China," Research in International Business and Finance, Elsevier, vol. 50(C), pages 246-251.
  • Handle: RePEc:eee:riibaf:v:50:y:2019:i:c:p:246-251
    DOI: 10.1016/j.ribaf.2019.06.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531918311450
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ribaf.2019.06.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    2. Khwaja, Asim Ijaz & Iyer, Rajkamal & Luttmer, Erzo F.P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Scholarly Articles 4448882, Harvard Kennedy School of Government.
    3. Dina Mayzlin & Jiwoong Shin, 2011. "Uninformative Advertising as an Invitation to Search," Marketing Science, INFORMS, vol. 30(4), pages 666-685, July.
    4. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    5. Kosuke Uetake & Ken ONISHI & Kei Kawai, 2013. "Signaling in Online Credit Markets," 2013 Meeting Papers 516, Society for Economic Dynamics.
    6. Crawford, Vincent P & Sobel, Joel, 1982. "Strategic Information Transmission," Econometrica, Econometric Society, vol. 50(6), pages 1431-1451, November.
    7. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Chao & Wang, Junbo & Wu, Chunchi & Zhang, Yue, 2023. "Voluntary disclosure in P2P lending: Information or hyperbole?," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    2. Chen, Rongda & Chen, Xinhao & Jin, Chenglu & Chen, Yiyang & Chen, Jiayi, 2020. "Credit rating of online lending borrowers using recovery rates," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 204-216.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maggie Rong Hu & Xiaoyang Li & Yang Shi, 2019. "Adverse Selection and Credit Certificates: Evidence from a P2P Platform," Working Papers id:13038, eSocialSciences.
    2. Hu, Maggie Rong & Li, Xiaoyang & Shi, Yang, 2019. "Adverse Selection and Credit Certificates: Evidence from a P2P Platform," ADBI Working Papers 942, Asian Development Bank Institute.
    3. Qizhi Tao & Yizhe Dong & Ziming Lin, 2017. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 19(3), pages 425-441, June.
    4. Qizhi Tao & Yizhe Dong & Ziming Lin, 0. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    5. Yeujun Yoon & Yu Li & Yan Feng, 2019. "Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data," Electronic Commerce Research, Springer, vol. 19(1), pages 131-158, March.
    6. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    7. Jianrong Yao & Jiarui Chen & June Wei & Yuangao Chen & Shuiqing Yang, 2019. "The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform," Electronic Commerce Research, Springer, vol. 19(1), pages 111-129, March.
    8. Chen, Xiao & Huang, Bihong & Shaban, Mohamed, 2022. "Naïve or sophisticated? Information disclosure and investment decisions in peer to peer lending," Journal of Corporate Finance, Elsevier, vol. 77(C).
    9. Andreas Hoegen & Dennis M. Steininger & Daniel Veit, 2018. "How do investors decide? An interdisciplinary review of decision-making in crowdfunding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 339-365, August.
    10. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    11. Wu, Yu & Zhang, Tong, 2021. "Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform," Finance Research Letters, Elsevier, vol. 40(C).
    12. Bryan Bollinger & Song Yao, 2018. "Risk transfer versus cost reduction on two-sided microfinance platforms," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 251-287, September.
    13. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    14. Gregor Dorfleitner & Eva-Maria Oswald & Rongxin Zhang, 2021. "From Credit Risk to Social Impact: On the Funding Determinants in Interest-Free Peer-to-Peer Lending," Journal of Business Ethics, Springer, vol. 170(2), pages 375-400, May.
    15. Xiong Xiong & Zhang Jin & Feng Xu & Jin Xi, 2016. "Review on Financial Innovations in Big Data Era," Journal of Systems Science and Information, De Gruyter, vol. 4(6), pages 489-504, December.
    16. Wang, Tong & Zhao, Sheng & Zhou, Mengqiu, 2022. "Does soft information in expert ratings curb information asymmetry? Evidence from crowdfunding and early transaction phases of Initial Coin offerings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    17. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
    18. Farag, Hisham & Johan, Sofia, 2021. "How alternative finance informs central themes in corporate finance," Journal of Corporate Finance, Elsevier, vol. 67(C).
    19. 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.
    20. Zhou, Yimin & Wei, Xu, 2020. "Joint liability loans in online peer-to-peer lending," Finance Research Letters, Elsevier, vol. 32(C).

    More about this item

    Keywords

    Peer-to-Peer lending; Signalling; Description length;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:50:y:2019:i:c:p:246-251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.