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Recommendation of investment portfolio for peer-to-peer lending with additional consideration of bidding period

Author

Listed:
  • Ki Taek Park

    (Yonsei University)

  • Hyejeong Yang

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

Peer-to-peer (P2P) lending has emerged as an alternative method of financing. Keeping pace with this development, many P2P lending studies have provided approaches to select investment portfolios for individual lenders. However, none of these approaches consider how long it takes for an individual loan to be fully funded so as to reduce the opportunity cost incurred due to delayed investment. In this paper, we propose a goal programming framework to develop an optimal P2P lending portfolio that considers not only the expected returns but also this opportunity cost for individual investors. First, for each loan proposal, a logistic regression model is used to predict the loan default probability while a Weibull regression is used to determine the opportunity cost incurred due to the time taken to obtain the loan. Next, goal programming is applied to construct a portfolio that minimizes the slack from the desired return on investment as well as the surplus from the preset opportunity cost due to a prolonged bidding period. The proposed approach is then applied to Prosper platform data and is expected to help investors’ portfolio decisions in the P2P lending market.

Suggested Citation

  • Ki Taek Park & Hyejeong Yang & So Young Sohn, 2022. "Recommendation of investment portfolio for peer-to-peer lending with additional consideration of bidding period," Annals of Operations Research, Springer, vol. 315(2), pages 1083-1105, August.
  • Handle: RePEc:spr:annopr:v:315:y:2022:i:2:d:10.1007_s10479-021-04300-z
    DOI: 10.1007/s10479-021-04300-z
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    References listed on IDEAS

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    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. Constantin Zopounidis & Michalis Doumpos & Kyriaki Kosmidou, 2018. "Preface: analytical models for financial modeling and risk management," Annals of Operations Research, Springer, vol. 266(1), pages 1-4, July.
    3. Conlin, Michael, 1999. "Peer group micro-lending programs in Canada and the United States," Journal of Development Economics, Elsevier, vol. 60(1), pages 249-269, October.
    4. Paul Belleflamme & Thomas Lambert & Armin Schwienbacher, 2013. "Individual crowdfunding practices," Venture Capital, Taylor & Francis Journals, vol. 15(4), pages 313-333, October.
    5. Lejeune, Miguel A. & Shen, Siqian, 2016. "Multi-objective probabilistically constrained programs with variable risk: Models for multi-portfolio financial optimization," European Journal of Operational Research, Elsevier, vol. 252(2), pages 522-539.
    6. Rafael Gomez & Eric Santor, 2003. "Do Peer Group Members Outperform Individual Borrowers? A Test of Peer Group Lending Using Canadian Micro-Credit Data," Staff Working Papers 03-33, Bank of Canada.
    7. Mollick, Ethan, 2014. "The dynamics of crowdfunding: An exploratory study," Journal of Business Venturing, Elsevier, vol. 29(1), pages 1-16.
    8. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    9. Jackson J. Mi & Tianxiao Hu & Luke Deer, 2018. "User Data Can Tell Defaulters in P2P Lending," Annals of Data Science, Springer, vol. 5(1), pages 59-67, March.
    10. 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.
    11. 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.
    12. Sonenshein, Scott & Herzenstein, Michal & Dholakia, Utpal M., 2011. "How accounts shape lending decisions through fostering perceived trustworthiness," Organizational Behavior and Human Decision Processes, Elsevier, vol. 115(1), pages 69-84, May.
    13. Dongwoo Kim, 2020. "The importance of detailed patterns of herding behaviour in a P2P lending market," Applied Economics Letters, Taylor & Francis Journals, vol. 27(2), pages 127-130, January.
    14. Xiangxiang Zeng & Li Liu & Stephen Leung & Jiangze Du & Xun Wang & Tao Li, 2017. "A decision support model for investment on P2P lending platform," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
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