#3158. Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework

October 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Finance;
Business, Management and Accounting (all);
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Abstract:
Peer-to-peer (P2P) lending has become popular amongst borrowers and individual investors. Despite the risks, many users are attracted by the easy and quick access to loans and the higher possible returns. The scholars treat the loan selection process in P2P lending as a portfolio optimization problem, minimizing risk. The research focuses on internal rate of return as the measure of return and use machine-learning algorithms to predict the default probabilities and calculate expected values. Kernel functions to obtain similarity weights of loans as the input of the optimization models is used. Two optimization models are tested and compared. The results sugest that the expected-value framework yields higher return.
Keywords:
expected-value framework; peer-to-peer lending; portfolio optimization

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