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

Ki Taek Park, Hyejeong Yang and So Young Sohn ()
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Ki Taek Park: Yonsei University
Hyejeong Yang: Yonsei University
So Young Sohn: Yonsei University

Annals of Operations Research, 2022, vol. 315, issue 2, No 20, 1083-1105

Abstract: 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.

Keywords: Analytics in banking; Goal programming; Multiple objective programming (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-021-04300-z

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