Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors

Ram D. Gopal (), Xiao Qiao (), Moris S. Strub () and Zonghao Yang ()
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Ram D. Gopal: Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom
Xiao Qiao: School of Data Science, City University of Hong Kong, Hong Kong SAR, China; and Hong Kong Institute for Data Science, Hong Kong SAR, China
Moris S. Strub: Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom
Zonghao Yang: School of Data Science, City University of Hong Kong, Hong Kong SAR, China

Information Systems Research, 2025, vol. 36, issue 1, 326-343

Abstract: Although online lending enjoyed explosive growth in the past decade, its market size remains small compared with other financial assets. The risk of losing money, stringent government regulations, and low awareness of the benefits have hampered the realization of the full potential of the online lending market. Because online loans are an emerging asset class, investors may not be aware of the investment performance of online loans compared with other assets, and it remains an open question whether online loans offer sufficiently attractive returns to warrant inclusion in an asset allocation decision. To attract lenders, platforms must provide an appealing investment opportunity which entails construction of portfolios of loans that investors find attractive. We propose general characteristics-based portfolio policy (GCPP), a novel framework to overcome the difficulties associated with portfolio construction of loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86%–13.08%, significantly outperforming an equal-weight portfolio of loans. We then address the question of whether online loans can earn competitive rates of return compared with traditional investment vehicles through six market indices covering stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared with traditional asset classes. Furthermore, the IRRs of the loan portfolios have small correlations with the benchmark index IRRs, pointing toward significant diversification benefits. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

Keywords: online lending; portfolio optimization; neural networks; fintech (search for similar items in EconPapers)
Date: 2025
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